{smcl}
{txt}{sf}{ul off}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\Joshua\Dropbox\Work\Networks and Economic Perceptions\Data and Do Files\Online Appendix D Output.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res} 6 Jun 2018, 11:00:08
{txt}
{com}.         /*the log file is in format .smcl, but can be read via txt editor*/
. 
. set more off
{txt}
{com}. 
. /***********************************************************************
> ************************************************************************
> ************************************************************************
> This do file contains the needed to replicate Online Appendix D where
> we investigate alterantive measurements of network disagreement. 
> 
> For each survey, the code will load and clean the data at hand using the
> commands included in the survey cleaning .do files and then do the 
> analyses in question. For those wishing to use this to replicate the analyses, 
> you will need to update the cd command below to point to where your data
> and .do files are located. 
> 
> Also note that there are two STATA modules that need to be intalled: 
> (1) esttab and (2) grc1leg. 
> 
> ssc install estout, replace
> ssc install grc1leg, replace
> 
> Note: The analyses here were run using STATA 13. Users of STATA 15 
> will likely get an error message when the esstab 
> command below runs, specifically: 
>         "equation / not found"
> This is because of the "rename" function in the command seems to be 
> broken in STATA 15. Deleting this line will fix matters. 
> 
> ************************************************************************
> ************************************************************************
> ***********************************************************************/
. 
. 
. /****************************************
> *****************************************
>                 1992 CNEP
> *****************************************
> ****************************************/       
. 
. *data cleaning*
. do "Data Cleaning - 1992 CNEP.do"
{txt}
{com}. **********************************************************************
. **********************************************************************
. ***********************1992 CNEP**************************************
. **********************************************************************
. **********************************************************************
. **********************************************************************
. 
. 
. **********************************************************************
. ****************************Data Cleaning****************************
. **********************************************************************
. **********************************************************************
. **********************************************************************
. **********************************************************************
. 
.         
. clear all
{txt}
{com}. use "1992 CNEP.dta"             
{txt}(CNEP NAT. STUDY: MAIN R, SPOUSE, D1 thru 5 MERGED FILE)

{com}. set more off
{txt}
{com}.         
.                 ************************************
.                 *********Economic Assessments*******
.                 ************************************
.         label def eco 1 "Worse" 2 "Same" 3 "Better"
{txt}
{com}.         
.         recode Z_US92_A_EconSit (1=3) (3=2) (5=1), gen(retro)
{txt}(1306 differences between Z_US92_A_EconSit and retro)

{com}.         label values retro eco
{txt}
{com}.         label var retro "Retrospective Assessments"
{txt}
{com}.         
.         recode Z_US92_A_EconSitFuture (1=3) (3=2) (5=1), gen(prosp)
{txt}(1296 differences between Z_US92_A_EconSitFuture and prosp)

{com}.         label var prosp "Prospective Assessments"
{txt}
{com}.         label values prosp eco
{txt}
{com}.         
.         
.                 ************************************
.                 *********Partisanship***************
.                 ************************************
. *7 pt scale     
.         label def part 1 "Strong Dem" 2 "Weak Dem" 3 "Lean Dem" 4 "Ind." 5 "Lean Rep" 6 "Weak rep" 7 "Strong Dem"
{txt}
{com}.         rename Z_US92_C_pid7 partyid
{res}{txt}
{com}.         label values partyid part
{txt}
{com}.         label var partyid "Party ID"
{txt}
{com}.         
. *3 pt scale
.         gen pid_3 = . 
{txt}(1323 missing values generated)

{com}.         replace pid_3 = 1 if partyid >=1 & partyid <= 3
{txt}(626 real changes made)

{com}.         replace pid_3 = 3 if partyid == 4
{txt}(101 real changes made)

{com}.         replace pid_3 = 2 if partyid >=5 & partyid <= 7
{txt}(578 real changes made)

{com}.         label var pid_3 "Party ID (Categorical)"
{txt}
{com}.         label def pi2 1 "Democrat" 3 "Independent" 2 "Republican"
{txt}
{com}.         label values pid_3 pi2
{txt}
{com}.         
. *Republican vs. Democrat
.         gen pid_2 = . 
{txt}(1323 missing values generated)

{com}.         replace pid_2 = 1 if partyid >=1 & partyid <= 3
{txt}(626 real changes made)

{com}.         replace pid_2 = 0 if partyid >=5 & partyid <= 7
{txt}(578 real changes made)

{com}.         label var pid_2 "PID" 
{txt}
{com}.         label def pi3 1 "Democrat" 0 "Republican"
{txt}
{com}.         label values pid_2 pi3
{txt}
{com}.         
. *Party of Incumbent President
.         *1 = Rep; 0 = Dem
.         gen partisan = . 
{txt}(1323 missing values generated)

{com}.         replace partisan = 1 if pid_2 == 0 
{txt}(578 real changes made)

{com}.         replace partisan = 0 if pid_2 == 1
{txt}(626 real changes made)

{com}.         label var partisan "Co-Partisan to Inc. President"
{txt}
{com}.         label def part1 1 "In-Partisan" 0 "Out-Partisan"
{txt}
{com}.         label values partisan part1
{txt}
{com}. 
. 
. *PID Strength (Non-Independents)*
.         gen pid_str = . 
{txt}(1323 missing values generated)

{com}.         replace pid_str = 1 if partyid == 3
{txt}(227 real changes made)

{com}.         replace pid_str = 1 if partyid == 5
{txt}(167 real changes made)

{com}.         replace pid_str = 2 if partyid == 2
{txt}(111 real changes made)

{com}.         replace pid_str = 2 if partyid == 6
{txt}(132 real changes made)

{com}.         replace pid_str = 3 if partyid == 1
{txt}(288 real changes made)

{com}.         replace pid_str = 3 if partyid == 7
{txt}(279 real changes made)

{com}.         label var pid_str "PID Str."
{txt}
{com}.         label def pi4 1 "Leaner" 2 "Weak" 3 "Strong"
{txt}
{com}.         label values pid_str pi4
{txt}
{com}. 
.         tab pid_str     

   {txt}PID Str. {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
     Leaner {c |}{res}        394       32.72       32.72
{txt}       Weak {c |}{res}        243       20.18       52.91
{txt}     Strong {c |}{res}        567       47.09      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,204      100.00
{txt}
{com}.         
.         recode partyid (1 7 = 4) (2 6 = 3) (3 5 = 2) (4 = 1), gen(pid_str_full)
{txt}(1305 differences between partyid and pid_str_full)

{com}.         label var pid_str_full "PID Str."
{txt}
{com}. 
.                 
.         
.                 *****************************************************
.                 *********Network Size and Disagreement***************
.                 *****************************************************
.                 
. *Network Size
.         foreach var in Z_US92_E_v185 Z_US92_E_v187 Z_US92_E_v189 Z_US92_E_v191 Z_US92_E_v193 {c -(}
{txt}  2{com}.                 codebook `var'
{txt}  3{com}.                 {c )-}

{txt}{hline}
{res}Z_US92_E_v185{right:DISCUSS IMPORTANT MATTERS W/ ANYONE}
{txt}{hline}

{col 19}type:  numeric ({res}byte{txt})
{ralign 22:label}:  {res:LABAR}, but {res:1} nonmissing value is not labeled

{col 18}range:  [{res}-1{txt},{res}5{txt}]{col 55}units:  {res}1
{col 10}{txt}unique values:  {res}3{col 51}{txt}missing .:  {res}0{txt}/{res}1323

{txt}{col 13}tabulation:  Freq.   Numeric  Label
{col 24}{res}      5{col 33}      -1{col 43}
{col 24}   1068{col 33}       1{col 43}{txt}yes
{col 24}{res}    250{col 33}       5{col 43}{txt}no

{txt}{hline}
{res}Z_US92_E_v187{right:DISCUSS W/ ANYONE ELSE}
{txt}{hline}

{col 19}type:  numeric ({res}byte{txt})
{ralign 22:label}:  {res:LABAR}

{col 18}range:  [{res}1{txt},{res}5{txt}]{col 55}units:  {res}1
{col 10}{txt}unique values:  {res}2{col 51}{txt}missing .:  {res}255{txt}/{res}1323

{txt}{col 13}tabulation:  Freq.   Numeric  Label
{col 24}{res}    887{col 33}       1{col 43}{txt}yes
{col 24}{res}    181{col 33}       5{col 43}{txt}no
{col 24}{res}    255{col 33}       .{col 43}

{txt}{hline}
{res}Z_US92_E_v189{right:DISCUSS W/ ANYONE ELSE}
{txt}{hline}

{col 19}type:  numeric ({res}byte{txt})
{ralign 22:label}:  {res:LABAR}

{col 18}range:  [{res}1{txt},{res}5{txt}]{col 55}units:  {res}1
{col 10}{txt}unique values:  {res}2{col 51}{txt}missing .:  {res}436{txt}/{res}1323

{txt}{col 13}tabulation:  Freq.   Numeric  Label
{col 24}{res}    614{col 33}       1{col 43}{txt}yes
{col 24}{res}    273{col 33}       5{col 43}{txt}no
{col 24}{res}    436{col 33}       .{col 43}

{txt}{hline}
{res}Z_US92_E_v191{right:DISCUSS W/ ANYONE ELSE}
{txt}{hline}

{col 19}type:  numeric ({res}byte{txt})
{ralign 22:label}:  {res:LABAR}

{col 18}range:  [{res}1{txt},{res}5{txt}]{col 55}units:  {res}1
{col 10}{txt}unique values:  {res}2{col 51}{txt}missing .:  {res}709{txt}/{res}1323

{txt}{col 13}tabulation:  Freq.   Numeric  Label
{col 24}{res}    395{col 33}       1{col 43}{txt}yes
{col 24}{res}    219{col 33}       5{col 43}{txt}no
{col 24}{res}    709{col 33}       .{col 43}

{txt}{hline}
{res}Z_US92_E_v193{right:PERSON TALKED WITH MOST ABOUT CAMPAIGN}
{txt}{hline}

{col 19}type:  numeric ({res}int{txt})
{ralign 22:label}:  {res:V616_A}, but {res:1} nonmissing value is not labeled

{col 18}range:  [{res}1{txt},{res}995{txt}]{col 55}units:  {res}1
{col 10}{txt}unique values:  {res}3{col 51}{txt}missing .:  {res}5{txt}/{res}1323

{txt}{col 13}tabulation:  Freq.   Numeric  Label
{col 24}{res}    787{col 33}       1{col 43}{txt}GIVES NAME
{col 24}{res}    530{col 33}       5{col 43}{txt}NO ONE/NO NAME GIVEN
{col 24}{res}      1{col 33}     995{col 43}
{col 24}      5{col 33}       .{col 43}
{txt}
{com}.                 
.         gen names = .
{txt}(1323 missing values generated)

{com}.         replace names =  4 if Z_US92_E_v191 == 1 & Z_US92_E_v189 == 1 & Z_US92_E_v187 == 1 & Z_US92_E_v185 == 1
{txt}(395 real changes made)

{com}.         replace names =  3 if Z_US92_E_v191 == 5 & Z_US92_E_v189 == 1 & Z_US92_E_v187 == 1 & Z_US92_E_v185 == 1
{txt}(219 real changes made)

{com}.         replace names =  2 if Z_US92_E_v189 == 5 & Z_US92_E_v187 == 1 & Z_US92_E_v185 == 1
{txt}(273 real changes made)

{com}.         replace names =  1 if Z_US92_E_v187 == 5 & Z_US92_E_v185 == 1
{txt}(181 real changes made)

{com}.         replace names =  0 if Z_US92_E_v185 == 5
{txt}(250 real changes made)

{com}.         label var names "# Listed Discussants"
{txt}
{com}.         
.         recode Z_US92_E_v193 (1=1) (5=0) (995 =.), gen(d5)
{txt}(531 differences between Z_US92_E_v193 and d5)

{com}.         
.         gen numgiven = names + d5
{txt}(6 missing values generated)

{com}.         label var numgiven "Network Size" 
{txt}
{com}.         
.         gen numgiven01 = numgiven/5
{txt}(6 missing values generated)

{com}.         label var numgiven01 "Network Size"
{txt}
{com}.         
.         
. *Candidate Disagreement
.         *discussant vote choice
.                 foreach var in  Z_US92_E_Disc1Part  Z_US92_E_Disc2Part  Z_US92_E_Disc3Part  Z_US92_E_Disc4Part  Z_US92_E_Disc5Part {c -(}
{txt}  2{com}.                         codebook `var'
{txt}  3{com}.                         {c )-}

{txt}{hline}
{res}Z_US92_E_Disc1Part{right:DISCUSS. 1 SUPPORT WHICH CAND}
{txt}{hline}

{col 19}type:  numeric ({res}byte{txt})
{ralign 22:label}:  {res:V526_A}, but {res:1} nonmissing value is not labeled

{col 18}range:  [{res}-1{txt},{res}9{txt}]{col 55}units:  {res}1
{col 10}{txt}unique values:  {res}10{col 51}{txt}missing .:  {res}250{txt}/{res}1323

{txt}{ralign 23: examples:}{col 26}{res}1{col 32}{txt}bush
{ralign 23: }{col 26}{res}2{col 32}{txt}clinton
{ralign 23: }{col 26}{res}2{col 32}{txt}clinton
{ralign 23: }{col 26}{res}8{col 32}{txt}dk

{txt}{hline}
{res}Z_US92_E_Disc2Part{right:DISCUSS. 2 SUPORT WHICH CAND}
{txt}{hline}

{col 19}type:  numeric ({res}byte{txt})
{ralign 22:label}:  {res:LABAF}, but {res:1} nonmissing value is not labeled

{col 18}range:  [{res}-1{txt},{res}9{txt}]{col 55}units:  {res}1
{col 10}{txt}unique values:  {res}9{col 51}{txt}missing .:  {res}431{txt}/{res}1323

{txt}{col 13}tabulation:  Freq.   Numeric  Label
{col 24}{res}      5{col 33}      -1{col 43}
{col 24}     13{col 33}       0{col 43}{txt}none
{col 24}{res}    328{col 33}       1{col 43}{txt}bush
{col 24}{res}    331{col 33}       2{col 43}{txt}clinton
{col 24}{res}    122{col 33}       3{col 43}{txt}perot
{col 24}{res}      2{col 33}       4{col 43}{txt}OTHER (SPECIFY)
{col 24}{res}      2{col 33}       7{col 43}{txt}CLINTON & PEROT
{col 24}{res}     86{col 33}       8{col 43}{txt}dk
{col 24}{res}      3{col 33}       9{col 43}{txt}refused
{col 24}{res}    431{col 33}       .{col 43}

{txt}{hline}
{res}Z_US92_E_Disc3Part{right:DISCUSS. 3 SUPPORT WHICH CAND}
{txt}{hline}

{col 19}type:  numeric ({res}byte{txt})
{ralign 22:label}:  {res:LABAF}, but {res:1} nonmissing value is not labeled

{col 18}range:  [{res}-1{txt},{res}9{txt}]{col 55}units:  {res}1
{col 10}{txt}unique values:  {res}10{col 51}{txt}missing .:  {res}704{txt}/{res}1323

{txt}{ralign 23: examples:}{col 26}{res}2{col 32}{txt}clinton
{ralign 23: }{col 26}{res}3{col 32}{txt}perot
{ralign 23: }{col 26}{res}.{col 32}{txt}
{ralign 23: }{col 26}{res}.{col 32}{txt}

{txt}{hline}
{res}Z_US92_E_Disc4Part{right:DISCUSS. 4 SUPPORT WHICH CAND}
{txt}{hline}

{col 19}type:  numeric ({res}byte{txt})
{ralign 22:label}:  {res:LABAO}, but {res:1} nonmissing value is not labeled

{col 18}range:  [{res}-1{txt},{res}9{txt}]{col 55}units:  {res}1
{col 10}{txt}unique values:  {res}9{col 51}{txt}missing .:  {res}923{txt}/{res}1323

{txt}{col 13}tabulation:  Freq.   Numeric  Label
{col 24}{res}      5{col 33}      -1{col 43}
{col 24}      7{col 33}       0{col 43}{txt}none
{col 24}{res}    118{col 33}       1{col 43}{txt}bush
{col 24}{res}    170{col 33}       2{col 43}{txt}clinton
{col 24}{res}     52{col 33}       3{col 43}{txt}perot
{col 24}{res}      1{col 33}       5{col 43}{txt}BUSH & CLINTON
{col 24}{res}      2{col 33}       6{col 43}{txt}BUSH & PEROT
{col 24}{res}     43{col 33}       8{col 43}{txt}dk
{col 24}{res}      2{col 33}       9{col 43}{txt}refused
{col 24}{res}    923{col 33}       .{col 43}

{txt}{hline}
{res}Z_US92_E_Disc5Part{right:DISCUSS. 5 SUPPORT WHICH CAND}
{txt}{hline}

{col 19}type:  numeric ({res}byte{txt})
{ralign 22:label}:  {res:LABAO}, but {res:1} nonmissing value is not labeled

{col 18}range:  [{res}-1{txt},{res}95{txt}]{col 55}units:  {res}1
{col 10}{txt}unique values:  {res}8{col 51}{txt}missing .:  {res}530{txt}/{res}1323

{txt}{col 13}tabulation:  Freq.   Numeric  Label
{col 24}{res}      5{col 33}      -1{col 43}
{col 24}     14{col 33}       0{col 43}{txt}none
{col 24}{res}    288{col 33}       1{col 43}{txt}bush
{col 24}{res}    320{col 33}       2{col 43}{txt}clinton
{col 24}{res}    112{col 33}       3{col 43}{txt}perot
{col 24}{res}     52{col 33}       8{col 43}{txt}dk
{col 24}{res}      1{col 33}       9{col 43}{txt}refused
{col 24}{res}      1{col 33}      95{col 43}{txt}inap
{col 24}{res}    530{col 33}       .{col 43}
{txt}
{com}.                 *-1 = . 
.                 *0 = none
.                 *1 = bush
.                 *2 = clinton
.                 *3 = perot
.                 *4 = other
.                 *7 = clinton & perot
.                 *8 = dk
.                 *9 = refused
.         
.         *respondent vote choice
.                 codebook H_Vote92

{txt}{hline}
{res}H_Vote92{right:R's VOTE IN PRESIDENTIAL ELECTION}
{txt}{hline}

{col 19}type:  numeric ({res}byte{txt})
{ralign 22:label}:  {res:H_VOTE92}

{col 18}range:  [{res}1{txt},{res}5{txt}]{col 55}units:  {res}1
{col 10}{txt}unique values:  {res}3{col 51}{txt}missing .:  {res}324{txt}/{res}1323

{txt}{col 13}tabulation:  Freq.   Numeric  Label
{col 24}{res}    444{col 33}       1{col 43}{txt}clinton
{col 24}{res}    167{col 33}       3{col 43}{txt}perot
{col 24}{res}    388{col 33}       5{col 43}{txt}bush
{col 24}{res}    324{col 33}       .{col 43}
{txt}
{com}.                         *1 = Clinton
.                         *3 Perot
.                         *5 = Bush
.         
.         *# Agreeing Respondents
.                 label def agre 1 "Agree" 0 "Disagree" 
{txt}
{com}.                         *agree: Bush/Bush; Clinton/Clinton; Perot/Perot
.                         *disagree: bush/clinton; clinton/bush; perot/bush; perot/bush; 
.                                 *bush/other; clinton/other; none, dk, refused and clinton/perot are dropped
.                 gen d1_agree = . 
{txt}(1323 missing values generated)

{com}.                 replace d1_agree = 1 if H_Vote92 == 1 & Z_US92_E_Disc1Part == 2
{txt}(243 real changes made)

{com}.                 replace d1_agree = 1 if H_Vote92 == 3 & Z_US92_E_Disc1Part == 3
{txt}(57 real changes made)

{com}.                 replace d1_agree = 1 if H_Vote92 == 5 & Z_US92_E_Disc1Part == 1
{txt}(215 real changes made)

{com}.                 replace d1_agree = 0 if H_Vote92 == 1 & Z_US92_E_Disc1Part == 1
{txt}(68 real changes made)

{com}.                 replace d1_agree = 0 if H_Vote92 == 1 & Z_US92_E_Disc1Part == 3
{txt}(29 real changes made)

{com}.                 replace d1_agree = 0 if H_Vote92 == 1 & Z_US92_E_Disc1Part == 4
{txt}(1 real change made)

{com}.                 replace d1_agree = 0 if H_Vote92 == 3 & Z_US92_E_Disc1Part == 1
{txt}(29 real changes made)

{com}.                 replace d1_agree = 0 if H_Vote92 == 3 & Z_US92_E_Disc1Part == 2
{txt}(48 real changes made)

{com}.                 replace d1_agree = 0 if H_Vote92 == 3 & Z_US92_E_Disc1Part == 4
{txt}(0 real changes made)

{com}.                 replace d1_agree = 0 if H_Vote92 == 5 & Z_US92_E_Disc1Part == 2
{txt}(60 real changes made)

{com}.                 replace d1_agree = 0 if H_Vote92 == 5 & Z_US92_E_Disc1Part == 3
{txt}(28 real changes made)

{com}.                 replace d1_agree = 0 if H_Vote92 == 5 & Z_US92_E_Disc1Part == 4
{txt}(2 real changes made)

{com}.                 label var d1_agree "D1: Same Cand Pref?"
{txt}
{com}.                 label values d1_agree agre
{txt}
{com}.         
.         
.         
.                 gen d2_agree = . 
{txt}(1323 missing values generated)

{com}.                 replace d2_agree = 1 if H_Vote92 == 1 & Z_US92_E_Disc2Part == 2
{txt}(193 real changes made)

{com}.                 replace d2_agree = 1 if H_Vote92 == 3 & Z_US92_E_Disc2Part == 3
{txt}(44 real changes made)

{com}.                 replace d2_agree = 1 if H_Vote92 == 5 & Z_US92_E_Disc2Part == 1
{txt}(199 real changes made)

{com}.                 replace d2_agree = 0 if H_Vote92 == 1 & Z_US92_E_Disc2Part == 1
{txt}(62 real changes made)

{com}.                 replace d2_agree = 0 if H_Vote92 == 1 & Z_US92_E_Disc2Part == 3
{txt}(29 real changes made)

{com}.                 replace d2_agree = 0 if H_Vote92 == 1 & Z_US92_E_Disc2Part == 4
{txt}(1 real change made)

{com}.                 replace d2_agree = 0 if H_Vote92 == 3 & Z_US92_E_Disc2Part == 1
{txt}(24 real changes made)

{com}.                 replace d2_agree = 0 if H_Vote92 == 3 & Z_US92_E_Disc2Part == 2
{txt}(29 real changes made)

{com}.                 replace d2_agree = 0 if H_Vote92 == 3 & Z_US92_E_Disc2Part == 4
{txt}(0 real changes made)

{com}.                 replace d2_agree = 0 if H_Vote92 == 5 & Z_US92_E_Disc2Part == 2
{txt}(36 real changes made)

{com}.                 replace d2_agree = 0 if H_Vote92 == 5 & Z_US92_E_Disc2Part == 3
{txt}(22 real changes made)

{com}.                 replace d2_agree = 0 if H_Vote92 == 5 & Z_US92_E_Disc2Part == 4
{txt}(0 real changes made)

{com}.                 label var d2_agree "D2: Same Cand Pref?"
{txt}
{com}.                 label values d2_agree agre
{txt}
{com}.         
.         
.                 gen d3_agree = . 
{txt}(1323 missing values generated)

{com}.                 replace d3_agree = 1 if H_Vote92 == 1 & Z_US92_E_Disc3Part == 2
{txt}(125 real changes made)

{com}.                 replace d3_agree = 1 if H_Vote92 == 3 & Z_US92_E_Disc3Part == 3
{txt}(22 real changes made)

{com}.                 replace d3_agree = 1 if H_Vote92 == 5 & Z_US92_E_Disc3Part == 1
{txt}(114 real changes made)

{com}.                 replace d3_agree = 0 if H_Vote92 == 1 & Z_US92_E_Disc3Part == 1
{txt}(48 real changes made)

{com}.                 replace d3_agree = 0 if H_Vote92 == 1 & Z_US92_E_Disc3Part == 3
{txt}(22 real changes made)

{com}.                 replace d3_agree = 0 if H_Vote92 == 1 & Z_US92_E_Disc3Part == 4
{txt}(0 real changes made)

{com}.                 replace d3_agree = 0 if H_Vote92 == 3 & Z_US92_E_Disc3Part == 1
{txt}(21 real changes made)

{com}.                 replace d3_agree = 0 if H_Vote92 == 3 & Z_US92_E_Disc3Part == 2
{txt}(24 real changes made)

{com}.                 replace d3_agree = 0 if H_Vote92 == 3 & Z_US92_E_Disc3Part == 4
{txt}(0 real changes made)

{com}.                 replace d3_agree = 0 if H_Vote92 == 5 & Z_US92_E_Disc3Part == 2
{txt}(40 real changes made)

{com}.                 replace d3_agree = 0 if H_Vote92 == 5 & Z_US92_E_Disc3Part == 3
{txt}(21 real changes made)

{com}.                 replace d3_agree = 0 if H_Vote92 == 5 & Z_US92_E_Disc3Part == 4
{txt}(2 real changes made)

{com}.                 label var d3_agree "D3: Same Cand Pref?"
{txt}
{com}.                 label values d3_agree agre
{txt}
{com}.                 
.                 gen d4_agree = . 
{txt}(1323 missing values generated)

{com}.                 replace d4_agree = 1 if H_Vote92 == 1 & Z_US92_E_Disc4Part == 2
{txt}(86 real changes made)

{com}.                 replace d4_agree = 1 if H_Vote92 == 3 & Z_US92_E_Disc4Part == 3
{txt}(20 real changes made)

{com}.                 replace d4_agree = 1 if H_Vote92 == 5 & Z_US92_E_Disc4Part == 1
{txt}(74 real changes made)

{com}.                 replace d4_agree = 0 if H_Vote92 == 1 & Z_US92_E_Disc4Part == 1
{txt}(27 real changes made)

{com}.                 replace d4_agree = 0 if H_Vote92 == 1 & Z_US92_E_Disc4Part == 3
{txt}(14 real changes made)

{com}.                 replace d4_agree = 0 if H_Vote92 == 1 & Z_US92_E_Disc4Part == 4
{txt}(0 real changes made)

{com}.                 replace d4_agree = 0 if H_Vote92 == 3 & Z_US92_E_Disc4Part == 1
{txt}(5 real changes made)

{com}.                 replace d4_agree = 0 if H_Vote92 == 3 & Z_US92_E_Disc4Part == 2
{txt}(23 real changes made)

{com}.                 replace d4_agree = 0 if H_Vote92 == 3 & Z_US92_E_Disc4Part == 4
{txt}(0 real changes made)

{com}.                 replace d4_agree = 0 if H_Vote92 == 5 & Z_US92_E_Disc4Part == 2
{txt}(28 real changes made)

{com}.                 replace d4_agree = 0 if H_Vote92 == 5 & Z_US92_E_Disc4Part == 3
{txt}(10 real changes made)

{com}.                 replace d4_agree = 0 if H_Vote92 == 5 & Z_US92_E_Disc4Part == 4
{txt}(0 real changes made)

{com}.                 label var d4_agree "D4: Same Cand Pref?"
{txt}
{com}.                 label values d4_agree agre
{txt}
{com}.                 
.                 gen d5_agree = . 
{txt}(1323 missing values generated)

{com}.                 replace d5_agree = 1 if H_Vote92 == 1 & Z_US92_E_Disc5Part == 2
{txt}(178 real changes made)

{com}.                 replace d5_agree = 1 if H_Vote92 == 3 & Z_US92_E_Disc5Part == 3
{txt}(46 real changes made)

{com}.                 replace d5_agree = 1 if H_Vote92 == 5 & Z_US92_E_Disc5Part == 1
{txt}(169 real changes made)

{com}.                 replace d5_agree = 0 if H_Vote92 == 1 & Z_US92_E_Disc5Part == 1
{txt}(54 real changes made)

{com}.                 replace d5_agree = 0 if H_Vote92 == 1 & Z_US92_E_Disc5Part == 3
{txt}(19 real changes made)

{com}.                 replace d5_agree = 0 if H_Vote92 == 1 & Z_US92_E_Disc5Part == 4
{txt}(0 real changes made)

{com}.                 replace d5_agree = 0 if H_Vote92 == 3 & Z_US92_E_Disc5Part == 1
{txt}(22 real changes made)

{com}.                 replace d5_agree = 0 if H_Vote92 == 3 & Z_US92_E_Disc5Part == 2
{txt}(31 real changes made)

{com}.                 replace d5_agree = 0 if H_Vote92 == 3 & Z_US92_E_Disc5Part == 4
{txt}(0 real changes made)

{com}.                 replace d5_agree = 0 if H_Vote92 == 5 & Z_US92_E_Disc5Part == 2
{txt}(40 real changes made)

{com}.                 replace d5_agree = 0 if H_Vote92 == 5 & Z_US92_E_Disc5Part == 3
{txt}(23 real changes made)

{com}.                 replace d5_agree = 0 if H_Vote92 == 5 & Z_US92_E_Disc5Part == 4
{txt}(0 real changes made)

{com}.                 label var d5_agree "D5: Same Cand Pref?"
{txt}
{com}.                 label values d5_agree agre
{txt}
{com}.                 
.                 foreach var in d1_agree d2_agree d3_agree d4_agree d5_agree {c -(}
{txt}  2{com}.                         tab `var'
{txt}  3{com}.                         {c )-}

   {txt}D1: Same {c |}
 Cand Pref? {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
   Disagree {c |}{res}        265       33.97       33.97
{txt}      Agree {c |}{res}        515       66.03      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        780      100.00

   {txt}D2: Same {c |}
 Cand Pref? {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
   Disagree {c |}{res}        203       31.77       31.77
{txt}      Agree {c |}{res}        436       68.23      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        639      100.00

   {txt}D3: Same {c |}
 Cand Pref? {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
   Disagree {c |}{res}        178       40.55       40.55
{txt}      Agree {c |}{res}        261       59.45      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        439      100.00

   {txt}D4: Same {c |}
 Cand Pref? {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
   Disagree {c |}{res}        107       37.28       37.28
{txt}      Agree {c |}{res}        180       62.72      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        287      100.00

   {txt}D5: Same {c |}
 Cand Pref? {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
   Disagree {c |}{res}        189       32.47       32.47
{txt}      Agree {c |}{res}        393       67.53      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        582      100.00
{txt}
{com}.         *Candidate **Disagreement
.                 label def disa 1 "Disagree" 0 "Agree"
{txt}
{com}.                         *agree: Bush/Bush; Clinton/Clinton; Perot/Perot
.                         *disagree: bush/clinton; clinton/bush; perot/bush; perot/bush; 
.                                 *bush/other; clinton/other; none, dk, refused and clinton/perot are dropped
.         
.                 gen d1_dagree = . 
{txt}(1323 missing values generated)

{com}.                 replace d1_dagree = 0 if H_Vote92 == 1 & Z_US92_E_Disc1Part == 2
{txt}(243 real changes made)

{com}.                 replace d1_dagree = 0 if H_Vote92 == 3 & Z_US92_E_Disc1Part == 3
{txt}(57 real changes made)

{com}.                 replace d1_dagree = 0 if H_Vote92 == 5 & Z_US92_E_Disc1Part == 1
{txt}(215 real changes made)

{com}.                 replace d1_dagree = 1 if H_Vote92 == 1 & Z_US92_E_Disc1Part == 1
{txt}(68 real changes made)

{com}.                 replace d1_dagree = 1 if H_Vote92 == 1 & Z_US92_E_Disc1Part == 3
{txt}(29 real changes made)

{com}.                 replace d1_dagree = 1 if H_Vote92 == 1 & Z_US92_E_Disc1Part == 4
{txt}(1 real change made)

{com}.                 replace d1_dagree = 1 if H_Vote92 == 3 & Z_US92_E_Disc1Part == 1
{txt}(29 real changes made)

{com}.                 replace d1_dagree = 1 if H_Vote92 == 3 & Z_US92_E_Disc1Part == 2
{txt}(48 real changes made)

{com}.                 replace d1_dagree = 1 if H_Vote92 == 3 & Z_US92_E_Disc1Part == 4
{txt}(0 real changes made)

{com}.                 replace d1_dagree = 1 if H_Vote92 == 5 & Z_US92_E_Disc1Part == 2
{txt}(60 real changes made)

{com}.                 replace d1_dagree = 1 if H_Vote92 == 5 & Z_US92_E_Disc1Part == 3
{txt}(28 real changes made)

{com}.                 replace d1_dagree = 1 if H_Vote92 == 5 & Z_US92_E_Disc1Part == 4
{txt}(2 real changes made)

{com}.                 label var d1_dagree "D1: Same Cand Pref?"
{txt}
{com}.                 label values d1_dagree disa
{txt}
{com}.         
.                         
.                 gen d2_dagree = . 
{txt}(1323 missing values generated)

{com}.                 replace d2_dagree = 0 if H_Vote92 == 1 & Z_US92_E_Disc2Part == 2
{txt}(193 real changes made)

{com}.                 replace d2_dagree = 0 if H_Vote92 == 3 & Z_US92_E_Disc2Part == 3
{txt}(44 real changes made)

{com}.                 replace d2_dagree = 0 if H_Vote92 == 5 & Z_US92_E_Disc2Part == 1
{txt}(199 real changes made)

{com}.                 
.                 replace d2_dagree = 1 if H_Vote92 == 1 & Z_US92_E_Disc2Part == 1
{txt}(62 real changes made)

{com}.                 replace d2_dagree = 1 if H_Vote92 == 1 & Z_US92_E_Disc2Part == 3
{txt}(29 real changes made)

{com}.                 replace d2_dagree = 1 if H_Vote92 == 1 & Z_US92_E_Disc2Part == 4
{txt}(1 real change made)

{com}.                 replace d2_dagree = 1 if H_Vote92 == 3 & Z_US92_E_Disc2Part == 1
{txt}(24 real changes made)

{com}.                 replace d2_dagree = 1 if H_Vote92 == 3 & Z_US92_E_Disc2Part == 2
{txt}(29 real changes made)

{com}.                 replace d2_dagree = 1 if H_Vote92 == 3 & Z_US92_E_Disc2Part == 4
{txt}(0 real changes made)

{com}.                 replace d2_dagree = 1 if H_Vote92 == 5 & Z_US92_E_Disc2Part == 2
{txt}(36 real changes made)

{com}.                 replace d2_dagree = 1 if H_Vote92 == 5 & Z_US92_E_Disc2Part == 3
{txt}(22 real changes made)

{com}.                 replace d2_dagree = 1 if H_Vote92 == 5 & Z_US92_E_Disc2Part == 4
{txt}(0 real changes made)

{com}.                 label var d2_dagree "D2: Same Cand Pref?"
{txt}
{com}.                 label values d2_dagree disa
{txt}
{com}.         
.         
.                 gen d3_dagree = . 
{txt}(1323 missing values generated)

{com}.                 replace d3_dagree = 0 if H_Vote92 == 1 & Z_US92_E_Disc3Part == 2
{txt}(125 real changes made)

{com}.                 replace d3_dagree = 0 if H_Vote92 == 3 & Z_US92_E_Disc3Part == 3
{txt}(22 real changes made)

{com}.                 replace d3_dagree = 0 if H_Vote92 == 5 & Z_US92_E_Disc3Part == 1
{txt}(114 real changes made)

{com}.                 
.                 replace d3_dagree = 1 if H_Vote92 == 1 & Z_US92_E_Disc3Part == 1
{txt}(48 real changes made)

{com}.                 replace d3_dagree = 1 if H_Vote92 == 1 & Z_US92_E_Disc3Part == 3
{txt}(22 real changes made)

{com}.                 replace d3_dagree = 1 if H_Vote92 == 1 & Z_US92_E_Disc3Part == 4
{txt}(0 real changes made)

{com}.                 replace d3_dagree = 1 if H_Vote92 == 3 & Z_US92_E_Disc3Part == 1
{txt}(21 real changes made)

{com}.                 replace d3_dagree = 1 if H_Vote92 == 3 & Z_US92_E_Disc3Part == 2
{txt}(24 real changes made)

{com}.                 replace d3_dagree = 1 if H_Vote92 == 3 & Z_US92_E_Disc3Part == 4
{txt}(0 real changes made)

{com}.                 replace d3_dagree = 1 if H_Vote92 == 5 & Z_US92_E_Disc3Part == 2
{txt}(40 real changes made)

{com}.                 replace d3_dagree = 1 if H_Vote92 == 5 & Z_US92_E_Disc3Part == 3
{txt}(21 real changes made)

{com}.                 replace d3_dagree = 1 if H_Vote92 == 5 & Z_US92_E_Disc3Part == 4
{txt}(2 real changes made)

{com}.                 label var d3_dagree "D3: Same Cand Pref?"
{txt}
{com}.                 label values d3_dagree disa
{txt}
{com}.                 
.                 gen d4_dagree = . 
{txt}(1323 missing values generated)

{com}.                 replace d4_dagree = 0 if H_Vote92 == 1 & Z_US92_E_Disc4Part == 2
{txt}(86 real changes made)

{com}.                 replace d4_dagree = 0 if H_Vote92 == 3 & Z_US92_E_Disc4Part == 3
{txt}(20 real changes made)

{com}.                 replace d4_dagree = 0 if H_Vote92 == 5 & Z_US92_E_Disc4Part == 1
{txt}(74 real changes made)

{com}.                 
.                 replace d4_dagree = 1 if H_Vote92 == 1 & Z_US92_E_Disc4Part == 1
{txt}(27 real changes made)

{com}.                 replace d4_dagree = 1 if H_Vote92 == 1 & Z_US92_E_Disc4Part == 3
{txt}(14 real changes made)

{com}.                 replace d4_dagree = 1 if H_Vote92 == 1 & Z_US92_E_Disc4Part == 4
{txt}(0 real changes made)

{com}.                 replace d4_dagree = 1 if H_Vote92 == 3 & Z_US92_E_Disc4Part == 1
{txt}(5 real changes made)

{com}.                 replace d4_dagree = 1 if H_Vote92 == 3 & Z_US92_E_Disc4Part == 2
{txt}(23 real changes made)

{com}.                 replace d4_dagree = 1 if H_Vote92 == 3 & Z_US92_E_Disc4Part == 4
{txt}(0 real changes made)

{com}.                 replace d4_dagree = 1 if H_Vote92 == 5 & Z_US92_E_Disc4Part == 2
{txt}(28 real changes made)

{com}.                 replace d4_dagree = 1 if H_Vote92 == 5 & Z_US92_E_Disc4Part == 3
{txt}(10 real changes made)

{com}.                 replace d4_dagree = 1 if H_Vote92 == 5 & Z_US92_E_Disc4Part == 4
{txt}(0 real changes made)

{com}.                 label var d4_dagree "D4: Same Cand Pref?"
{txt}
{com}.                 label values d4_dagree disa
{txt}
{com}.                 
.                 gen d5_dagree = . 
{txt}(1323 missing values generated)

{com}.                 replace d5_dagree = 0 if H_Vote92 == 1 & Z_US92_E_Disc5Part == 2
{txt}(178 real changes made)

{com}.                 replace d5_dagree = 0 if H_Vote92 == 3 & Z_US92_E_Disc5Part == 3
{txt}(46 real changes made)

{com}.                 replace d5_dagree = 0 if H_Vote92 == 5 & Z_US92_E_Disc5Part == 1
{txt}(169 real changes made)

{com}.                 
.                 replace d5_dagree = 1 if H_Vote92 == 1 & Z_US92_E_Disc5Part == 1
{txt}(54 real changes made)

{com}.                 replace d5_dagree = 1 if H_Vote92 == 1 & Z_US92_E_Disc5Part == 3
{txt}(19 real changes made)

{com}.                 replace d5_dagree = 1 if H_Vote92 == 1 & Z_US92_E_Disc5Part == 4
{txt}(0 real changes made)

{com}.                 replace d5_dagree = 1 if H_Vote92 == 3 & Z_US92_E_Disc5Part == 1
{txt}(22 real changes made)

{com}.                 replace d5_dagree = 1 if H_Vote92 == 3 & Z_US92_E_Disc5Part == 2
{txt}(31 real changes made)

{com}.                 replace d5_dagree = 1 if H_Vote92 == 3 & Z_US92_E_Disc5Part == 4
{txt}(0 real changes made)

{com}.                 replace d5_dagree = 1 if H_Vote92 == 5 & Z_US92_E_Disc5Part == 2
{txt}(40 real changes made)

{com}.                 replace d5_dagree = 1 if H_Vote92 == 5 & Z_US92_E_Disc5Part == 3
{txt}(23 real changes made)

{com}.                 replace d5_dagree = 1 if H_Vote92 == 5 & Z_US92_E_Disc5Part == 4
{txt}(0 real changes made)

{com}.                 label var d5_dagree "D5: Same Cand Pref?"
{txt}
{com}.                 label values d5_dagree disa
{txt}
{com}.                         
.         
.         *#Disagreeing Respondents
.                 egen agree = rowtotal(d1_agree d2_agree d3_agree d4_agree d5_agree), missing
{txt}(413 missing values generated)

{com}.                 egen disagree = rowtotal(d1_dagree d2_dagree d3_dagree d4_dagree d5_dagree), missing
{txt}(413 missing values generated)

{com}.                 label var agree "# Agreeing Partners"
{txt}
{com}.                 label var disagree "# Disagreeing Partners"
{txt}
{com}.                                         
.         *Exposure to Disagreement Measures
.                 *Summary Scale
.                         *See Lupton and Thornton: Exposure = D - A
.                         gen disagree_total = disagree - agree
{txt}(413 missing values generated)

{com}.                         label var disagree_total "Network Disagreement"
{txt}
{com}.                 *Divided by network size
.                         *See Lupton and Thonrton: (D-A)/(D+A)
.                         gen disagree_avg = [disagree - agree]/[disagree + agree]
{txt}(413 missing values generated)

{com}.                         label var disagree_avg "Network Disagreement"
{txt}
{com}.                                                 
.                 *Standardized*
.                         foreach var in disagree_total disagree_avg {c -(}
{txt}  2{com}.                                 summ `var'
{txt}  3{com}.                                 gen `var'01 = (`var' - r(min))/(r(max)-r(min))
{txt}  4{com}.                         {c )-}

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
disagree_t~l {c |}{res}       910   -.9263736    2.014269         -5          5
{txt}(413 missing values generated)

    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
disagree_avg {c |}{res}       910   -.3464469    .6853855         -1          1
{txt}(413 missing values generated)

{com}.                         
.                         label var disagree_total01 "Network Disagreement"
{txt}
{com}.                         label var disagree_avg01 "Network Disagreement"
{txt}
{com}.                         
.                 *Categorical
.                         gen cand_cat1 = .
{txt}(1323 missing values generated)

{com}.                         replace cand_cat1 = 1 if numgiven == 0
{txt}(114 real changes made)

{com}.                         replace cand_cat1 = 2 if disagree_total >= -5 & disagree_total <= -1
{txt}(577 real changes made)

{com}.                         replace cand_cat1 = 3 if disagree_total == 0
{txt}(107 real changes made)

{com}.                         replace cand_cat1 = 4 if disagree_total >=1 & disagree_total <= 5
{txt}(226 real changes made)

{com}.                         label var cand_cat1 "Network Type"
{txt}
{com}.                         label def can 1 "No Discussants" 2 "Agree > Disagree" 3 "Ambivalent" 4 "Agree < Disagree"
{txt}
{com}.                         label values cand_cat1 can
{txt}
{com}.                         
.                         gen cand_cat2 = .
{txt}(1323 missing values generated)

{com}.                         replace cand_cat2 = 1 if disagree_total >= -5 & disagree_total <= -1
{txt}(577 real changes made)

{com}.                         replace cand_cat2 = 2 if disagree_total == 0
{txt}(107 real changes made)

{com}.                         replace cand_cat2 = 3 if disagree_total >=1 & disagree_total <= 5
{txt}(226 real changes made)

{com}.                         label var cand_cat2 "Network Type"
{txt}
{com}.                         label def canb 1 "Agree > Disagree" 2 "Ambivalent" 3 "Agree < Disagree"
{txt}
{com}.                         label values cand_cat2 canb
{txt}
{com}. 
.         *Diversity Measure*
.                 *From Nir (2005): [(Agree+Disagree)/2] - |A-D|
.                         gen network_ambiv = [(agree+disagree)/2] - abs(agree - disagree)
{txt}(413 missing values generated)

{com}.                         label var network_ambiv "Network Political Diversity"
{txt}
{com}.                 
.                         summ network_ambiv

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
network_am~v {c |}{res}       910   -.2851648    1.176526       -2.5          2
{txt}
{com}.                         gen network_ambiv01=(network_ambiv - r(min))/(r(max)-r(min))
{txt}(413 missing values generated)

{com}.                         label var network_ambiv01 "Network Political Diversity"
{txt}
{com}. 
.                         
.                 
. *General Disagreement
.         foreach var in Z_US92_E_Disc1Agre Z_US92_E_Disc2Agre Z_US92_E_Disc3Agre Z_US92_E_Disc4Agre Z_US92_E_Disc5Agre {c -(}
{txt}  2{com}.                 codebook `var' 
{txt}  3{com}.                 {c )-}

{txt}{hline}
{res}Z_US92_E_Disc1Agre{right:DISAGREE ABOUT POLITICS, DISCUSS. 1}
{txt}{hline}

{col 19}type:  numeric ({res}int{txt})
{ralign 22:label}:  {res:LABY}, but {res:2} nonmissing values are not labeled

{col 18}range:  [{res}-1{txt},{res}995{txt}]{col 55}units:  {res}1
{col 10}{txt}unique values:  {res}7{col 51}{txt}missing .:  {res}286{txt}/{res}1323

{txt}{col 13}tabulation:  Freq.   Numeric  Label
{col 24}{res}      5{col 33}      -1{col 43}
{col 24}    102{col 33}       1{col 43}{txt}often
{col 24}{res}    494{col 33}       2{col 43}{txt}sometimes
{col 24}{res}    347{col 33}       3{col 43}{txt}rarely
{col 24}{res}     85{col 33}       4{col 43}{txt}never
{col 24}{res}      2{col 33}       9{col 43}{txt}refused
{col 24}{res}      2{col 33}     995{col 43}
{col 24}    286{col 33}       .{col 43}

{txt}{hline}
{res}Z_US92_E_Disc2Agre{right:DISAGREE ABOUT POLITICS, DISCUSS. 2}
{txt}{hline}

{col 19}type:  numeric ({res}int{txt})
{ralign 22:label}:  {res:V530_A}, but {res:2} nonmissing values are not labeled

{col 18}range:  [{res}-1{txt},{res}995{txt}]{col 55}units:  {res}1
{col 10}{txt}unique values:  {res}7{col 51}{txt}missing .:  {res}470{txt}/{res}1323

{txt}{col 13}tabulation:  Freq.   Numeric  Label
{col 24}{res}      5{col 33}      -1{col 43}
{col 24}     87{col 33}       1{col 43}{txt}often
{col 24}{res}    361{col 33}       2{col 43}{txt}sometimes
{col 24}{res}    323{col 33}       3{col 43}{txt}rarely
{col 24}{res}     74{col 33}       4{col 43}{txt}never
{col 24}{res}      1{col 33}       8{col 43}{txt}dk
{col 24}{res}      2{col 33}     995{col 43}
{col 24}    470{col 33}       .{col 43}

{txt}{hline}
{res}Z_US92_E_Disc3Agre{right:DISAGREE ABOUT POLITICS, DISCUSS. 3}
{txt}{hline}

{col 19}type:  numeric ({res}int{txt})
{ralign 22:label}:  {res:LABY}, but {res:2} nonmissing values are not labeled

{col 18}range:  [{res}-1{txt},{res}995{txt}]{col 55}units:  {res}1
{col 10}{txt}unique values:  {res}7{col 51}{txt}missing .:  {res}737{txt}/{res}1323

{txt}{col 13}tabulation:  Freq.   Numeric  Label
{col 24}{res}      5{col 33}      -1{col 43}
{col 24}     69{col 33}       1{col 43}{txt}often
{col 24}{res}    250{col 33}       2{col 43}{txt}sometimes
{col 24}{res}    210{col 33}       3{col 43}{txt}rarely
{col 24}{res}     50{col 33}       4{col 43}{txt}never
{col 24}{res}      1{col 33}       9{col 43}{txt}refused
{col 24}{res}      1{col 33}     995{col 43}
{col 24}    737{col 33}       .{col 43}

{txt}{hline}
{res}Z_US92_E_Disc4Agre{right:DISAGREE ABOUT POLITICS, DISCUSS. 4}
{txt}{hline}

{col 19}type:  numeric ({res}byte{txt})
{ralign 22:label}:  {res:LABAI}, but {res:1} nonmissing value is not labeled

{col 18}range:  [{res}-1{txt},{res}4{txt}]{col 55}units:  {res}1
{col 10}{txt}unique values:  {res}5{col 51}{txt}missing .:  {res}946{txt}/{res}1323

{txt}{col 13}tabulation:  Freq.   Numeric  Label
{col 24}{res}      5{col 33}      -1{col 43}
{col 24}     40{col 33}       1{col 43}{txt}often
{col 24}{res}    170{col 33}       2{col 43}{txt}sometimes
{col 24}{res}    129{col 33}       3{col 43}{txt}rarely
{col 24}{res}     33{col 33}       4{col 43}{txt}never
{col 24}{res}    946{col 33}       .{col 43}

{txt}{hline}
{res}Z_US92_E_Disc5Agre{right:DISAGREE ABOUT POLITICS, DISCUSS. 5}
{txt}{hline}

{col 19}type:  numeric ({res}int{txt})
{ralign 22:label}:  {res:LABAI}, but {res:2} nonmissing values are not labeled

{col 18}range:  [{res}-1{txt},{res}995{txt}]{col 55}units:  {res}1
{col 10}{txt}unique values:  {res}8{col 51}{txt}missing .:  {res}541{txt}/{res}1323

{txt}{col 13}tabulation:  Freq.   Numeric  Label
{col 24}{res}      5{col 33}      -1{col 43}
{col 24}    100{col 33}       1{col 43}{txt}often
{col 24}{res}    339{col 33}       2{col 43}{txt}sometimes
{col 24}{res}    263{col 33}       3{col 43}{txt}rarely
{col 24}{res}     70{col 33}       4{col 43}{txt}never
{col 24}{res}      2{col 33}       8{col 43}{txt}dk
{col 24}{res}      1{col 33}      95{col 43}{txt}inap
{col 24}{res}      2{col 33}     995{col 43}
{col 24}    541{col 33}       .{col 43}
{txt}
{com}. 
.                 
.         *Disagreement Scale
.         label def gend 1 "Never" 2 "Rarely" 3 "Sometimes" 4 "Often"
{txt}
{com}.         recode   Z_US92_E_Disc1Agre  (-1=.) (1=4) (2=3) (3=2) (4=1) (8=.) (9=.) (95=.) (995=.), gen(d1_genagree)
{txt}(1037 differences between Z_US92_E_Disc1Agre and d1_genagree)

{com}.         recode   Z_US92_E_Disc2Agre  (-1=.) (1=4) (2=3) (3=2) (4=1) (8=.) (9=.) (95=.) (995=.), gen(d2_genagree)
{txt}(853 differences between Z_US92_E_Disc2Agre and d2_genagree)

{com}.         recode   Z_US92_E_Disc3Agre  (-1=.) (1=4) (2=3) (3=2) (4=1) (8=.) (9=.) (95=.) (995=.), gen(d3_genagree)
{txt}(586 differences between Z_US92_E_Disc3Agre and d3_genagree)

{com}.         recode   Z_US92_E_Disc4Agre  (-1=.) (1=4) (2=3) (3=2) (4=1) (8=.) (9=.) (95=.) (995=.), gen(d4_genagree)
{txt}(377 differences between Z_US92_E_Disc4Agre and d4_genagree)

{com}.         recode   Z_US92_E_Disc5Agre  (-1=.) (1=4) (2=3) (3=2) (4=1) (8=.) (9=.) (95=.) (995=.), gen(d5_genagree)
{txt}(782 differences between Z_US92_E_Disc5Agre and d5_genagree)

{com}.                 
.         foreach var in d1_genagree d2_genagree d3_genagree d4_genagree d5_genagree {c -(}
{txt}  2{com}.                 label values `var' gend
{txt}  3{com}.                 tab `var'
{txt}  4{com}.                 {c )-}

  {txt}RECODE of {c |}
Z_US92_E_Di {c |}
    sc1Agre {c |}
  (DISAGREE {c |}
      ABOUT {c |}
  POLITICS, {c |}
DISCUSS. 1) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
      Never {c |}{res}         85        8.27        8.27
{txt}     Rarely {c |}{res}        347       33.75       42.02
{txt}  Sometimes {c |}{res}        494       48.05       90.08
{txt}      Often {c |}{res}        102        9.92      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,028      100.00

  {txt}RECODE of {c |}
Z_US92_E_Di {c |}
    sc2Agre {c |}
  (DISAGREE {c |}
      ABOUT {c |}
  POLITICS, {c |}
DISCUSS. 2) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
      Never {c |}{res}         74        8.76        8.76
{txt}     Rarely {c |}{res}        323       38.22       46.98
{txt}  Sometimes {c |}{res}        361       42.72       89.70
{txt}      Often {c |}{res}         87       10.30      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        845      100.00

  {txt}RECODE of {c |}
Z_US92_E_Di {c |}
    sc3Agre {c |}
  (DISAGREE {c |}
      ABOUT {c |}
  POLITICS, {c |}
DISCUSS. 3) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
      Never {c |}{res}         50        8.64        8.64
{txt}     Rarely {c |}{res}        210       36.27       44.91
{txt}  Sometimes {c |}{res}        250       43.18       88.08
{txt}      Often {c |}{res}         69       11.92      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        579      100.00

  {txt}RECODE of {c |}
Z_US92_E_Di {c |}
    sc4Agre {c |}
  (DISAGREE {c |}
      ABOUT {c |}
  POLITICS, {c |}
DISCUSS. 4) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
      Never {c |}{res}         33        8.87        8.87
{txt}     Rarely {c |}{res}        129       34.68       43.55
{txt}  Sometimes {c |}{res}        170       45.70       89.25
{txt}      Often {c |}{res}         40       10.75      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        372      100.00

  {txt}RECODE of {c |}
Z_US92_E_Di {c |}
    sc5Agre {c |}
  (DISAGREE {c |}
      ABOUT {c |}
  POLITICS, {c |}
DISCUSS. 5) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
      Never {c |}{res}         70        9.07        9.07
{txt}     Rarely {c |}{res}        263       34.07       43.13
{txt}  Sometimes {c |}{res}        339       43.91       87.05
{txt}      Often {c |}{res}        100       12.95      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        772      100.00
{txt}
{com}.                                 
.         *Avgerage Scale
.         egen genavg = rowmean(d1_genagree d2_genagree d3_genagree d4_genagree d5_genagree)
{txt}(135 missing values generated)

{com}.         label var genavg "General Disagreement in Network"
{txt}
{com}.                 
.                 *Connection with 'disagreeidate' disagreement
.                 pwcorr disagree_total disagree_avg genavg, sig

             {txt}{c |} disagr~l disagr~g   genavg
{hline 13}{c +}{hline 27}
disagree_t~l {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
disagree_avg {c |} {res}  0.8389   1.0000 
             {txt}{c |}{res}   0.0000
             {txt}{c |}
      genavg {c |} {res}  0.2725   0.2762   1.0000 
             {txt}{c |}{res}   0.0000   0.0000
             {txt}{c |}

{com}.                 pwcorr disagree_total disagree_avg genavg, sig  

             {txt}{c |} disagr~l disagr~g   genavg
{hline 13}{c +}{hline 27}
disagree_t~l {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
disagree_avg {c |} {res}  0.8389   1.0000 
             {txt}{c |}{res}   0.0000
             {txt}{c |}
      genavg {c |} {res}  0.2725   0.2762   1.0000 
             {txt}{c |}{res}   0.0000   0.0000
             {txt}{c |}

{com}.         
. *Combined Index (Lupton and Thornton)
.         /**fn. 5: A = sum(ai * si) where a = 1 if agree and s = agreement weight
>                         D = sum(di * si) where d = 1 if disagree and s = disagreement weight
>                         Disagreement = D - A**/
.         
.         *"Agreement" Coding
.                 foreach var in   Z_US92_E_Disc1Agre Z_US92_E_Disc2Agre Z_US92_E_Disc3Agre        Z_US92_E_Disc4Agre Z_US92_E_Disc5Agre {c -(}
{txt}  2{com}.                         recode `var' (-1=.) (8=.) (9=.) (95=.) (995=.), gen(`var'_ag)
{txt}  3{com}.                         {c )-}
{txt}(9 differences between Z_US92_E_Disc1Agre and Z_US92_E_Disc1Agre_ag)
(8 differences between Z_US92_E_Disc2Agre and Z_US92_E_Disc2Agre_ag)
(7 differences between Z_US92_E_Disc3Agre and Z_US92_E_Disc3Agre_ag)
(5 differences between Z_US92_E_Disc4Agre and Z_US92_E_Disc4Agre_ag)
(10 differences between Z_US92_E_Disc5Agre and Z_US92_E_Disc5Agre_ag)

{com}. 
.         *Agree Scale
.                 gen a1 = d1_agree * Z_US92_E_Disc1Agre_ag
{txt}(555 missing values generated)

{com}.                 gen a2 = d2_agree * Z_US92_E_Disc2Agre_ag
{txt}(695 missing values generated)

{com}.                 gen a3 = d3_agree * Z_US92_E_Disc3Agre_ag
{txt}(903 missing values generated)

{com}.                 gen a4 = d4_agree * Z_US92_E_Disc4Agre_ag
{txt}(1041 missing values generated)

{com}.                 gen a5 = d5_agree * Z_US92_E_Disc5Agre_ag
{txt}(748 missing values generated)

{com}.                 egen agree_weight = rowtotal(a1 a2 a3 a4 a5), missing
{txt}(418 missing values generated)

{com}.         *Disagree Scale
.                 gen d1 = d1_dagree * d1_genagree
{txt}(555 missing values generated)

{com}.                 gen d2 = d2_dagree * d2_genagree
{txt}(695 missing values generated)

{com}.                 gen d3 = d3_dagree * d3_genagree
{txt}(903 missing values generated)

{com}.                 gen d4 = d4_dagree *  d4_genagree
{txt}(1041 missing values generated)

{com}.                 gen d5a = d5_dagree *  d5_genagree
{txt}(748 missing values generated)

{com}.                 egen disagree_weight = rowtotal(d1 d2 d3 d4 d5), missing
{txt}(5 missing values generated)

{com}.         
.         *Exposure to Disagreement
.                 gen disagree_total_weight = disagree_weight - agree_weight
{txt}(418 missing values generated)

{com}.                 gen disagree_avg_weight = disagree_total_weight/(disagree+agree)
{txt}(418 missing values generated)

{com}.                 
.                 foreach var in disagree_total_weight disagree_avg_weight {c -(}
{txt}  2{com}.                         summ `var' 
{txt}  3{com}.                         gen `var'01 = (`var' - r(min))/(r(max)-r(min))
{txt}  4{com}.                         tab `var'01
{txt}  5{com}.                 {c )-}

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
disagree_t~t {c |}{res}       905   -1.963536    5.290436        -17         15
{txt}(418 missing values generated)

disagree_to {c |}
tal_weight0 {c |}
          1 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}          2        0.22        0.22
{txt}     .03125 {c |}{res}          2        0.22        0.44
{txt}      .0625 {c |}{res}          1        0.11        0.55
{txt}     .09375 {c |}{res}          9        0.99        1.55
{txt}       .125 {c |}{res}          6        0.66        2.21
{txt}     .15625 {c |}{res}         14        1.55        3.76
{txt}      .1875 {c |}{res}         31        3.43        7.18
{txt}     .21875 {c |}{res}         17        1.88        9.06
{txt}        .25 {c |}{res}         23        2.54       11.60
{txt}     .28125 {c |}{res}         29        3.20       14.81
{txt}      .3125 {c |}{res}         38        4.20       19.01
{txt}     .34375 {c |}{res}         50        5.52       24.53
{txt}       .375 {c |}{res}         45        4.97       29.50
{txt}     .40625 {c |}{res}         48        5.30       34.81
{txt}      .4375 {c |}{res}         81        8.95       43.76
{txt}     .46875 {c |}{res}         99       10.94       54.70
{txt}         .5 {c |}{res}         82        9.06       63.76
{txt}     .53125 {c |}{res}         39        4.31       68.07
{txt}      .5625 {c |}{res}         66        7.29       75.36
{txt}     .59375 {c |}{res}         53        5.86       81.22
{txt}       .625 {c |}{res}         37        4.09       85.30
{txt}     .65625 {c |}{res}         34        3.76       89.06
{txt}      .6875 {c |}{res}         22        2.43       91.49
{txt}     .71875 {c |}{res}         25        2.76       94.25
{txt}        .75 {c |}{res}         20        2.21       96.46
{txt}     .78125 {c |}{res}         11        1.22       97.68
{txt}      .8125 {c |}{res}          5        0.55       98.23
{txt}     .84375 {c |}{res}          7        0.77       99.01
{txt}       .875 {c |}{res}          2        0.22       99.23
{txt}     .90625 {c |}{res}          5        0.55       99.78
{txt}      .9375 {c |}{res}          1        0.11       99.89
{txt}          1 {c |}{res}          1        0.11      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        905      100.00

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
disagree_a~t {c |}{res}       905   -.7196133    1.737424         -4          4
{txt}(418 missing values generated)

disagree_av {c |}
 g_weight01 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}          9        0.99        0.99
{txt}   .0416667 {c |}{res}          2        0.22        1.22
{txt}      .0625 {c |}{res}          8        0.88        2.10
{txt}       .075 {c |}{res}          2        0.22        2.32
{txt}   .0833333 {c |}{res}          2        0.22        2.54
{txt}         .1 {c |}{res}          2        0.22        2.76
{txt}       .125 {c |}{res}         66        7.29       10.06
{txt}        .15 {c |}{res}          7        0.77       10.83
{txt}     .15625 {c |}{res}         19        2.10       12.93
{txt}   .1666667 {c |}{res}         11        1.22       14.14
{txt}       .175 {c |}{res}          6        0.66       14.81
{txt}      .1875 {c |}{res}         31        3.43       18.23
{txt}         .2 {c |}{res}         10        1.10       19.34
{txt}   .2083333 {c |}{res}         15        1.66       20.99
{txt}     .21875 {c |}{res}          8        0.88       21.88
{txt}       .225 {c |}{res}         10        1.10       22.98
{txt}        .25 {c |}{res}        105       11.60       34.59
{txt}       .275 {c |}{res}          6        0.66       35.25
{txt}     .28125 {c |}{res}         12        1.33       36.57
{txt}   .2916667 {c |}{res}          5        0.55       37.13
{txt}         .3 {c |}{res}          7        0.77       37.90
{txt}      .3125 {c |}{res}         26        2.87       40.77
{txt}       .325 {c |}{res}          5        0.55       41.33
{txt}   .3333333 {c |}{res}         10        1.10       42.43
{txt}     .34375 {c |}{res}          9        0.99       43.43
{txt}        .35 {c |}{res}          6        0.66       44.09
{txt}       .375 {c |}{res}         76        8.40       52.49
{txt}         .4 {c |}{res}          8        0.88       53.37
{txt}     .40625 {c |}{res}         11        1.22       54.59
{txt}   .4166667 {c |}{res}         13        1.44       56.02
{txt}       .425 {c |}{res}         10        1.10       57.13
{txt}      .4375 {c |}{res}         23        2.54       59.67
{txt}        .45 {c |}{res}          7        0.77       60.44
{txt}   .4583333 {c |}{res}         15        1.66       62.10
{txt}     .46875 {c |}{res}          5        0.55       62.65
{txt}       .475 {c |}{res}         10        1.10       63.76
{txt}         .5 {c |}{res}         39        4.31       68.07
{txt}       .525 {c |}{res}         10        1.10       69.17
{txt}     .53125 {c |}{res}          8        0.88       70.06
{txt}   .5416667 {c |}{res}         14        1.55       71.60
{txt}        .55 {c |}{res}         12        1.33       72.93
{txt}      .5625 {c |}{res}         22        2.43       75.36
{txt}       .575 {c |}{res}          7        0.77       76.13
{txt}   .5833333 {c |}{res}         14        1.55       77.68
{txt}     .59375 {c |}{res}          5        0.55       78.23
{txt}         .6 {c |}{res}          8        0.88       79.12
{txt}       .625 {c |}{res}         53        5.86       84.97
{txt}        .65 {c |}{res}          5        0.55       85.52
{txt}     .65625 {c |}{res}          4        0.44       85.97
{txt}   .6666667 {c |}{res}          1        0.11       86.08
{txt}       .675 {c |}{res}          9        0.99       87.07
{txt}      .6875 {c |}{res}         14        1.55       88.62
{txt}         .7 {c |}{res}          4        0.44       89.06
{txt}   .7083333 {c |}{res}         11        1.22       90.28
{txt}     .71875 {c |}{res}          6        0.66       90.94
{txt}       .725 {c |}{res}          2        0.22       91.16
{txt}        .75 {c |}{res}         27        2.98       94.14
{txt}       .775 {c |}{res}          1        0.11       94.25
{txt}     .78125 {c |}{res}          2        0.22       94.48
{txt}   .7916666 {c |}{res}          3        0.33       94.81
{txt}         .8 {c |}{res}          4        0.44       95.25
{txt}      .8125 {c |}{res}          6        0.66       95.91
{txt}   .8333334 {c |}{res}          5        0.55       96.46
{txt}     .84375 {c |}{res}          1        0.11       96.57
{txt}       .875 {c |}{res}         17        1.88       98.45
{txt}     .90625 {c |}{res}          1        0.11       98.56
{txt}   .9166666 {c |}{res}          2        0.22       98.78
{txt}      .9375 {c |}{res}          2        0.22       99.01
{txt}          1 {c |}{res}          9        0.99      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        905      100.00
{txt}
{com}.                 
.                 label var disagree_total_weight "Network Disagreement"
{txt}
{com}.                 label var disagree_total_weight "Network Disagreement"
{txt}
{com}.         
.                 label var disagree_avg_weight "Network Disagreement"
{txt}
{com}.                 label var disagree_avg_weight "Network Disagreement"
{txt}
{com}.         
. 
. *Combined Index (Alternative)
.         gen d1_both = d1_dagree + d1_genagree
{txt}(555 missing values generated)

{com}.         gen d2_both = d2_dagree + d2_genagree
{txt}(695 missing values generated)

{com}.         gen d3_both = d3_dagree + d3_genagree
{txt}(903 missing values generated)

{com}.         gen d4_both = d4_dagree + d4_genagree
{txt}(1041 missing values generated)

{com}.         gen d5_both = d5_dagree + d5_genagree
{txt}(748 missing values generated)

{com}.         
.         egen dis_both = rowmean(d1_both d2_both d3_both d4_both d5_both)
{txt}(418 missing values generated)

{com}.         label var dis_both "Index of Network Disagreement"
{txt}
{com}.         summ dis_both, detail

                {txt}Index of Network Disagreement
{hline 61}
      Percentiles      Smallest
 1%    {res}        1              1
{txt} 5%    {res} 1.666667              1
{txt}10%    {res}        2              1       {txt}Obs         {res}        905
{txt}25%    {res} 2.333333              1       {txt}Sum of Wgt. {res}        905

{txt}50%    {res}        3                      {txt}Mean          {res} 2.906538
                        {txt}Largest       Std. Dev.     {res} .7788423
{txt}75%    {res}      3.5              5
{txt}90%    {res}        4              5       {txt}Variance      {res} .6065953
{txt}95%    {res}        4              5       {txt}Skewness      {res}-.1712941
{txt}99%    {res}      4.5              5       {txt}Kurtosis      {res}  2.87142
{txt}
{com}.         tab dis_both

   {txt}Index of {c |}
    Network {c |}
Disagreemen {c |}
          t {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}         28        3.09        3.09
{txt}       1.25 {c |}{res}          3        0.33        3.43
{txt}   1.333333 {c |}{res}          1        0.11        3.54
{txt}        1.4 {c |}{res}          2        0.22        3.76
{txt}        1.5 {c |}{res}          7        0.77        4.53
{txt}        1.6 {c |}{res}          2        0.22        4.75
{txt}   1.666667 {c |}{res}          6        0.66        5.41
{txt}       1.75 {c |}{res}          4        0.44        5.86
{txt}        1.8 {c |}{res}          1        0.11        5.97
{txt}          2 {c |}{res}        121       13.37       19.34
{txt}        2.2 {c |}{res}          9        0.99       20.33
{txt}       2.25 {c |}{res}         27        2.98       23.31
{txt}   2.333333 {c |}{res}         19        2.10       25.41
{txt}        2.4 {c |}{res}         16        1.77       27.18
{txt}        2.5 {c |}{res}         52        5.75       32.93
{txt}        2.6 {c |}{res}         15        1.66       34.59
{txt}   2.666667 {c |}{res}         34        3.76       38.34
{txt}       2.75 {c |}{res}         18        1.99       40.33
{txt}        2.8 {c |}{res}         17        1.88       42.21
{txt}          3 {c |}{res}        199       21.99       64.20
{txt}        3.2 {c |}{res}         23        2.54       66.74
{txt}       3.25 {c |}{res}         21        2.32       69.06
{txt}   3.333333 {c |}{res}         30        3.31       72.38
{txt}        3.4 {c |}{res}         19        2.10       74.48
{txt}        3.5 {c |}{res}         44        4.86       79.34
{txt}        3.6 {c |}{res}         16        1.77       81.10
{txt}   3.666667 {c |}{res}         25        2.76       83.87
{txt}       3.75 {c |}{res}         12        1.33       85.19
{txt}        3.8 {c |}{res}         14        1.55       86.74
{txt}          4 {c |}{res}         89        9.83       96.57
{txt}        4.2 {c |}{res}          4        0.44       97.02
{txt}       4.25 {c |}{res}          6        0.66       97.68
{txt}   4.333333 {c |}{res}          5        0.55       98.23
{txt}        4.5 {c |}{res}          9        0.99       99.23
{txt}        4.6 {c |}{res}          1        0.11       99.34
{txt}          5 {c |}{res}          6        0.66      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        905      100.00
{txt}
{com}.         
.                 
.                 ***************************************************
.                 *****************Control Variables*****************
.                 ***************************************************
.         
. *Ideology & Ideological Strength
.         gen ideology = C_LRSelf_F
{txt}(8 missing values generated)

{com}.         recode ideology (999=.)
{txt}(ideology: 33 changes made)

{com}.         label def ido 1 "Most Liberal" 10 "Most Conservative"
{txt}
{com}.         label values ideology ido
{txt}
{com}.         label var ideology "Ideology"
{txt}
{com}.         
.         recode ideology  (1=5) (2=4) (3=3) (4=2) (5=1)  ///
>                 (6=1) (7=2) (8=3) (9=4) (10=5), gen(ideol_str)
{txt}(1192 differences between ideology and ideol_str)

{com}.                 
.         foreach var in ideology ideol_str {c -(}
{txt}  2{com}.                 summ `var'
{txt}  3{com}.                 gen `var'01 = (`var' - r(min))/(r(max)-r(min))
{txt}  4{com}.                 {c )-}

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 4}ideology {c |}{res}      1282    5.874415    2.257571          1         10
{txt}(41 missing values generated)

    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 3}ideol_str {c |}{res}      1282    2.329173    1.374204          1          5
{txt}(41 missing values generated)

{com}.         label var ideology01 "Ideology" 
{txt}
{com}.         label var ideol_str "Ideological Extremity"
{txt}
{com}.         
.         
. *Gender
.                 recode L_Gender (1=0) (5=1), gen(gender)
{txt}(1318 differences between L_Gender and gender)

{com}.                 label var gender "Gender"
{txt}
{com}.                 label def gen1 1 "Female" 0 "Male"
{txt}
{com}.                 label values gender gen1
{txt}
{com}.                 
. 
. *Age
.                 gen age = L_Age_F
{txt}(5 missing values generated)

{com}.                 replace age = . if age > 900
{txt}(5 real changes made, 5 to missing)

{com}.                 label var age "Age"
{txt}
{com}.                 summ age

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 9}age {c |}{res}      1313    45.23686     17.2886         18         92
{txt}
{com}.                 
.                 summ age

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 9}age {c |}{res}      1313    45.23686     17.2886         18         92
{txt}
{com}.                 gen age01 = (age - r(min))/(r(max)-r(min))
{txt}(10 missing values generated)

{com}.                 label var age01 "Age"
{txt}
{com}. 
. 
. *Education
.                 gen educ = . 
{txt}(1323 missing values generated)

{com}.                 replace educ = 1 if Z_US92_L_Education_F == 3
{txt}(129 real changes made)

{com}.                 replace educ = 2 if Z_US92_L_Education_F == 4
{txt}(417 real changes made)

{com}.                 replace educ = 3 if Z_US92_L_Education_F == 5
{txt}(342 real changes made)

{com}.                 replace educ = 4 if Z_US92_L_Education_F == 6
{txt}(235 real changes made)

{com}.                 replace educ = 4 if Z_US92_L_Education_F ==7
{txt}(193 real changes made)

{com}.                 label var educ "Education"
{txt}
{com}.                 label def edu 1 "< HS" 2 "HS" 3 "Some College" 4 "College+"
{txt}
{com}.                 label values educ edu
{txt}
{com}.                 summ educ 

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 8}educ {c |}{res}      1316     2.81231    .9998633          1          4
{txt}
{com}.                 tab educ

   {txt}Education {c |}      Freq.     Percent        Cum.
{hline 13}{c +}{hline 35}
        < HS {c |}{res}        129        9.80        9.80
{txt}          HS {c |}{res}        417       31.69       41.49
{txt}Some College {c |}{res}        342       25.99       67.48
{txt}    College+ {c |}{res}        428       32.52      100.00
{txt}{hline 13}{c +}{hline 35}
       Total {c |}{res}      1,316      100.00
{txt}
{com}.                 
.                 summ educ 

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 8}educ {c |}{res}      1316     2.81231    .9998633          1          4
{txt}
{com}.                 gen educ01 = (educ - r(min))/(r(max)-r(min))
{txt}(7 missing values generated)

{com}.                 label var educ01 "Education"
{txt}
{com}. 
. *Race
.                 gen race = . 
{txt}(1323 missing values generated)

{com}.                 replace race = 1 if Z_US92_L_Race  == 1
{txt}(1139 real changes made)

{com}.                 replace race = 2 if Z_US92_L_Race  == 2
{txt}(111 real changes made)

{com}.                 replace race = 3 if Z_US92_L_Race  >=3 & Z_US92_L_Race <= 5
{txt}(63 real changes made)

{com}.                 label def rac1 1 "White" 2 "Black" 3 "Other"
{txt}
{com}.                 label values race rac1
{txt}
{com}.                 label var race "Race"
{txt}
{com}.                 
.                 tabulate race, gen(race_)

       {txt}Race {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
      White {c |}{res}      1,139       86.75       86.75
{txt}      Black {c |}{res}        111        8.45       95.20
{txt}      Other {c |}{res}         63        4.80      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,313      100.00
{txt}
{com}.                 label var race_2 "Black"
{txt}
{com}.                 label var race_3 "Other Race"
{txt}
{com}.                 label def r2 1 "Black" 
{txt}
{com}.                 label def r3 1 "Other Race"
{txt}
{com}.                 label values race_2 r2
{txt}
{com}.                 label values race_3 r3
{txt}
{com}.                 
.                 
. *Income 
.         gen income = L_Income_F2
{txt}(5 missing values generated)

{com}.         mvdecode income, mv(998=.a \ 999=.b)
      {txt}income:{res}{col 15}90{txt} missing values generated

{com}.         label def inc 1 "<15,000" 2 "15-24,999" 3 "25-34,999" 4 "34-50,000" 5 "50,001-75" 6 "75000+"
{txt}
{com}.         label values income inc
{txt}
{com}.         summ income

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 6}income {c |}{res}      1228    3.346091    1.597653          1          6
{txt}
{com}.         label var income "Family Income"
{txt}
{com}.         
.         summ income 

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 6}income {c |}{res}      1228    3.346091    1.597653          1          6
{txt}
{com}.         gen income01 = (income - r(min))/(r(max)-r(min))
{txt}(95 missing values generated)

{com}.         label var income01 "Family Income"
{txt}
{com}.         
.         
. *Marital Status
.         gen marital = .
{txt}(1323 missing values generated)

{com}.         replace marital = 1 if L_Married == 1
{txt}(728 real changes made)

{com}.         replace marital = 0 if L_Married >=2 & L_Married <= 7
{txt}(589 real changes made)

{com}.         label var marital "Marriage Status"
{txt}
{com}.         label def mar 1 "Married" 0 "Not Married" 
{txt}
{com}.         label values marital mar
{txt}
{com}.         
.         
. *Employment Status
.         gen employment = L_WorkStat
{txt}(6 missing values generated)

{com}.         label var employment "Employment Status"
{txt}
{com}.         replace employment = . if employment == 0 
{txt}(59 real changes made, 59 to missing)

{com}.         label def emp 1 "Employed" 2 "Unemployed" 3 "Retired" 4 "Keeping House" 5 "Student"
{txt}
{com}.         label values employment emp
{txt}
{com}.         
.         recode employment (1=1) (2=2) (3=3) (4=4) (5=4) , gen(employed)
{txt}(86 differences between employment and employed)

{com}.         label var employed "Employment Status"
{txt}
{com}.         label def emp1 1 "Employed" 2 "Unemployed" 3 "Retired" 4 "Keeping House/Student"
{txt}
{com}.         label values employment emp1
{txt}
{com}.         
.         
.         tabulate employment, gen(employ)

    {txt}Employment Status {c |}      Freq.     Percent        Cum.
{hline 22}{c +}{hline 35}
             Employed {c |}{res}        774       61.53       61.53
{txt}           Unemployed {c |}{res}         50        3.97       65.50
{txt}              Retired {c |}{res}        230       18.28       83.78
{txt}Keeping House/Student {c |}{res}        118        9.38       93.16
{txt}                    5 {c |}{res}         86        6.84      100.00
{txt}{hline 22}{c +}{hline 35}
                Total {c |}{res}      1,258      100.00
{txt}
{com}.         label var employ2 "Unemployed" 
{txt}
{com}.         label var employ3 "Retired" 
{txt}
{com}.         label var employ4 "Keeping House" 
{txt}
{com}.         label var employ5 "Student"
{txt}
{com}.         label def e2 1 "Unemployed"
{txt}
{com}.         label def e3 1 "Retired" 
{txt}
{com}.         label def e4 1 "Keeping House" 
{txt}
{com}.         label def e5 1 "Student"
{txt}
{com}.         label values employ2 e2
{txt}
{com}.         label values employ3 e3
{txt}
{com}.         label values employ4 e4
{txt}
{com}.         label values employ5 e5
{txt}
{com}. 
.         
.         
.         
. *Campaign Interest
.         recode H_InterestCam (1=3) (3=2) (5=1), gen(camp_interest)
{txt}(1315 differences between H_InterestCam and camp_interest)

{com}.         label var camp_interest "Campaign Interest"
{txt}
{com}.         label def int 1 "Not Much" 2 "Somewhat" 3 "Very Much"
{txt}
{com}.         label values camp_interest int
{txt}
{com}. 
.         recode  D_PapAtent_F (0=1) (1=2) (2=3) (3=4) (995=.) (999=.), gen(newspaper)
{txt}(1317 differences between D_PapAtent_F and newspaper)

{com}.         recode D_TVAtent (1=4) (2=3) (3=2) (4=1) (5=.), gen(tv)
{txt}(1316 differences between D_TVAtent and tv)

{com}.         recode Z_US92_D_CamMag (1=4) (2=3) (3=2) (4=1), gen(mag)
{txt}(1318 differences between Z_US92_D_CamMag and mag)

{com}.         recode Z_US92_D_CamLocalTV  (1=4) (2=3) (3=2) (4=1) (5=1) , gen(localtv)
{txt}(1316 differences between Z_US92_D_CamLocalTV and localtv)

{com}.         recode Z_US92_D_CamRadio (1=4) (2=3) (3=2) (4=1) (5=1) , gen(radio)
{txt}(1318 differences between Z_US92_D_CamRadio and radio)

{com}. 
.         factor camp_interest newspaper tv,  pcf
{txt}(obs=1291)

Factor analysis/correlation{col 52}Number of obs    = {res}    1291
{col 5}{txt}Method: principal-component factors{col 52}Retained factors = {res}       1
{col 5}{txt}Rotation: (unrotated){col 52}Number of params = {res}       3

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      1.76933      1.12949            0.5898       0.5898
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      0.63985      0.04902            0.2133       0.8031
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}      0.59082            .            0.1969       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}3{txt})  ={res}  518.46{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:camp_inter~t}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7735}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4017}}}{space 1}
{space 4}{space 0}{ralign 12:newspaper}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7515}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4353}}}{space 1}
{space 4}{space 0}{ralign 12:tv}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7787}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3937}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{c  BT}{hline 14}

{com}.                 *loadings from 0.75-0.77
.                 *EV: 1.77
.                 *proportion: 0.57
.         predict factor1 
{txt}(regression scoring assumed)

{p 0 0 2}Scoring coefficients (method = regression){p_end}

{space 4}{hline 13}{c  TT}{hline 10}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}
{space 4}{hline 13}{c   +}{hline 10}
{space 4}{space 0}{ralign 12:camp_inter~t}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.43718}}}{space 1}
{space 4}{space 0}{ralign 12:newspaper}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.42471}}}{space 1}
{space 4}{space 0}{ralign 12:tv}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.44009}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}


{com}.         label var factor1 "News Attention & Interest"
{txt}
{com}.         rename factor1 news_att
{res}{txt}
{com}.         
.         summ news_att

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 4}news_att {c |}{res}      1291    1.25e-09           1  -2.812994   1.396825
{txt}
{com}.         gen news_att01 = (news_att - r(min))/(r(max) - r(min))
{txt}(32 missing values generated)

{com}.         label var news_att01 "News Attention & Interest"
{txt}
{com}. 
.         
. *Discussant Knowledge
.         foreach var in  Z_US92_E_Disc1Know  Z_US92_E_Disc2Know  Z_US92_E_Disc3Know  Z_US92_E_Disc4Know  Z_US92_E_Disc5Know  {c -(}
{txt}  2{com}.                 tab `var'
{txt}  3{com}.                 codebook `var'
{txt}  4{com}.                 {c )-}

       {txt}DISCUSS. 1 {c |}
        POLITICAL {c |}
        KNOWLEDGE {c |}      Freq.     Percent        Cum.
{hline 18}{c +}{hline 35}
               -1 {c |}{res}          5        0.47        0.47
{txt}     A GREAT DEAL {c |}{res}        320       29.82       30.29
{txt}AN AVERAGE AMOUNT {c |}{res}        677       63.09       93.38
{txt}         NOT MUCH {c |}{res}         61        5.68       99.07
{txt}               dk {c |}{res}          8        0.75       99.81
{txt}          refused {c |}{res}          2        0.19      100.00
{txt}{hline 18}{c +}{hline 35}
            Total {c |}{res}      1,073      100.00

{txt}{hline}
{res}Z_US92_E_Disc1Know{right:DISCUSS. 1 POLITICAL KNOWLEDGE}
{txt}{hline}

{col 19}type:  numeric ({res}byte{txt})
{ralign 22:label}:  {res:V524_A}, but {res:1} nonmissing value is not labeled

{col 18}range:  [{res}-1{txt},{res}9{txt}]{col 55}units:  {res}1
{col 10}{txt}unique values:  {res}6{col 51}{txt}missing .:  {res}250{txt}/{res}1323

{txt}{col 13}tabulation:  Freq.   Numeric  Label
{col 24}{res}      5{col 33}      -1{col 43}
{col 24}    320{col 33}       1{col 43}{txt}A GREAT DEAL
{col 24}{res}    677{col 33}       3{col 43}{txt}AN AVERAGE AMOUNT
{col 24}{res}     61{col 33}       5{col 43}{txt}NOT MUCH
{col 24}{res}      8{col 33}       8{col 43}{txt}dk
{col 24}{res}      2{col 33}       9{col 43}{txt}refused
{col 24}{res}    250{col 33}       .{col 43}

       {txt}DISCUSS. 2 {c |}
        POLITICAL {c |}
        KNOWLEDGE {c |}      Freq.     Percent        Cum.
{hline 18}{c +}{hline 35}
               -1 {c |}{res}          5        0.56        0.56
{txt}     A GREAT DEAL {c |}{res}        237       26.57       27.13
{txt}AN AVERAGE AMOUNT {c |}{res}        579       64.91       92.04
{txt}         NOT MUCH {c |}{res}         64        7.17       99.22
{txt}               dk {c |}{res}          7        0.78      100.00
{txt}{hline 18}{c +}{hline 35}
            Total {c |}{res}        892      100.00

{txt}{hline}
{res}Z_US92_E_Disc2Know{right:DISCUSS. 2 POLITICAL KNOWLEDGE}
{txt}{hline}

{col 19}type:  numeric ({res}byte{txt})
{ralign 22:label}:  {res:LABAE}, but {res:1} nonmissing value is not labeled

{col 18}range:  [{res}-1{txt},{res}8{txt}]{col 55}units:  {res}1
{col 10}{txt}unique values:  {res}5{col 51}{txt}missing .:  {res}431{txt}/{res}1323

{txt}{col 13}tabulation:  Freq.   Numeric  Label
{col 24}{res}      5{col 33}      -1{col 43}
{col 24}    237{col 33}       1{col 43}{txt}A GREAT DEAL
{col 24}{res}    579{col 33}       3{col 43}{txt}AN AVERAGE AMOUNT
{col 24}{res}     64{col 33}       5{col 43}{txt}NOT MUCH
{col 24}{res}      7{col 33}       8{col 43}{txt}dk
{col 24}{res}    431{col 33}       .{col 43}

       {txt}DISCUSS. 3 {c |}
        POLITICAL {c |}
        KNOWLEDGE {c |}      Freq.     Percent        Cum.
{hline 18}{c +}{hline 35}
               -1 {c |}{res}          5        0.81        0.81
{txt}     A GREAT DEAL {c |}{res}        180       29.08       29.89
{txt}AN AVERAGE AMOUNT {c |}{res}        368       59.45       89.34
{txt}         NOT MUCH {c |}{res}         61        9.85       99.19
{txt}               dk {c |}{res}          4        0.65       99.84
{txt}          refused {c |}{res}          1        0.16      100.00
{txt}{hline 18}{c +}{hline 35}
            Total {c |}{res}        619      100.00

{txt}{hline}
{res}Z_US92_E_Disc3Know{right:DISCUSS. 3 POLITICAL KNOWLEDGE}
{txt}{hline}

{col 19}type:  numeric ({res}byte{txt})
{ralign 22:label}:  {res:LABAE}, but {res:1} nonmissing value is not labeled

{col 18}range:  [{res}-1{txt},{res}9{txt}]{col 55}units:  {res}1
{col 10}{txt}unique values:  {res}6{col 51}{txt}missing .:  {res}704{txt}/{res}1323

{txt}{col 13}tabulation:  Freq.   Numeric  Label
{col 24}{res}      5{col 33}      -1{col 43}
{col 24}    180{col 33}       1{col 43}{txt}A GREAT DEAL
{col 24}{res}    368{col 33}       3{col 43}{txt}AN AVERAGE AMOUNT
{col 24}{res}     61{col 33}       5{col 43}{txt}NOT MUCH
{col 24}{res}      4{col 33}       8{col 43}{txt}dk
{col 24}{res}      1{col 33}       9{col 43}{txt}refused
{col 24}{res}    704{col 33}       .{col 43}

       {txt}DISCUSS. 4 {c |}
        POLITICAL {c |}
        KNOWLEDGE {c |}      Freq.     Percent        Cum.
{hline 18}{c +}{hline 35}
               -1 {c |}{res}          5        1.25        1.25
{txt}     A GREAT DEAL {c |}{res}        104       26.00       27.25
{txt}AN AVERAGE AMOUNT {c |}{res}        250       62.50       89.75
{txt}         NOT MUCH {c |}{res}         38        9.50       99.25
{txt}               dk {c |}{res}          3        0.75      100.00
{txt}{hline 18}{c +}{hline 35}
            Total {c |}{res}        400      100.00

{txt}{hline}
{res}Z_US92_E_Disc4Know{right:DISCUSS. 4 POLITICAL KNOWLEDGE}
{txt}{hline}

{col 19}type:  numeric ({res}byte{txt})
{ralign 22:label}:  {res:LABAN}, but {res:1} nonmissing value is not labeled

{col 18}range:  [{res}-1{txt},{res}8{txt}]{col 55}units:  {res}1
{col 10}{txt}unique values:  {res}5{col 51}{txt}missing .:  {res}923{txt}/{res}1323

{txt}{col 13}tabulation:  Freq.   Numeric  Label
{col 24}{res}      5{col 33}      -1{col 43}
{col 24}    104{col 33}       1{col 43}{txt}A GREAT DEAL
{col 24}{res}    250{col 33}       3{col 43}{txt}AN AVERAGE AMOUNT
{col 24}{res}     38{col 33}       5{col 43}{txt}NOT MUCH
{col 24}{res}      3{col 33}       8{col 43}{txt}dk
{col 24}{res}    923{col 33}       .{col 43}

       {txt}DISCUSS. 5 {c |}
        POLITICAL {c |}
        KNOWLEDGE {c |}      Freq.     Percent        Cum.
{hline 18}{c +}{hline 35}
               -1 {c |}{res}          5        0.63        0.63
{txt}     A GREAT DEAL {c |}{res}        262       33.04       33.67
{txt}AN AVERAGE AMOUNT {c |}{res}        466       58.76       92.43
{txt}         NOT MUCH {c |}{res}         57        7.19       99.62
{txt}               dk {c |}{res}          2        0.25       99.87
{txt}             inap {c |}{res}          1        0.13      100.00
{txt}{hline 18}{c +}{hline 35}
            Total {c |}{res}        793      100.00

{txt}{hline}
{res}Z_US92_E_Disc5Know{right:DISCUSS. 5 POLITICAL KNOWLEDGE}
{txt}{hline}

{col 19}type:  numeric ({res}byte{txt})
{ralign 22:label}:  {res:LABAN}, but {res:1} nonmissing value is not labeled

{col 18}range:  [{res}-1{txt},{res}95{txt}]{col 55}units:  {res}1
{col 10}{txt}unique values:  {res}6{col 51}{txt}missing .:  {res}530{txt}/{res}1323

{txt}{col 13}tabulation:  Freq.   Numeric  Label
{col 24}{res}      5{col 33}      -1{col 43}
{col 24}    262{col 33}       1{col 43}{txt}A GREAT DEAL
{col 24}{res}    466{col 33}       3{col 43}{txt}AN AVERAGE AMOUNT
{col 24}{res}     57{col 33}       5{col 43}{txt}NOT MUCH
{col 24}{res}      2{col 33}       8{col 43}{txt}dk
{col 24}{res}      1{col 33}      95{col 43}{txt}inap
{col 24}{res}    530{col 33}       .{col 43}
{txt}
{com}.         
.         recode Z_US92_E_Disc1Know (1=3) (3=2) (5=1), gen(disc1_knowl)
{txt}(1058 differences between Z_US92_E_Disc1Know and disc1_knowl)

{com}.         recode Z_US92_E_Disc2Know (1=3) (3=2) (5=1), gen(disc2_knowl)
{txt}(880 differences between Z_US92_E_Disc2Know and disc2_knowl)

{com}.         recode Z_US92_E_Disc3Know (1=3) (3=2) (5=1), gen(disc3_knowl)
{txt}(609 differences between Z_US92_E_Disc3Know and disc3_knowl)

{com}.         recode Z_US92_E_Disc4Know (1=3) (3=2) (5=1), gen(disc4_knowl)
{txt}(392 differences between Z_US92_E_Disc4Know and disc4_knowl)

{com}.         recode Z_US92_E_Disc5Know (1=3) (3=2) (5=1), gen(disc5_knowl)
{txt}(785 differences between Z_US92_E_Disc5Know and disc5_knowl)

{com}.         foreach var in disc1_knowl disc2_knowl disc3_knowl disc4_knowl disc5_knowl {c -(}
{txt}  2{com}.                 tab `var'
{txt}  3{com}.                 {c )-}

  {txt}RECODE of {c |}
Z_US92_E_Di {c |}
    sc1Know {c |}
(DISCUSS. 1 {c |}
  POLITICAL {c |}
 KNOWLEDGE) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
         -1 {c |}{res}          5        0.47        0.47
{txt}          1 {c |}{res}         61        5.68        6.15
{txt}          2 {c |}{res}        677       63.09       69.25
{txt}          3 {c |}{res}        320       29.82       99.07
{txt}          8 {c |}{res}          8        0.75       99.81
{txt}          9 {c |}{res}          2        0.19      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,073      100.00

  {txt}RECODE of {c |}
Z_US92_E_Di {c |}
    sc2Know {c |}
(DISCUSS. 2 {c |}
  POLITICAL {c |}
 KNOWLEDGE) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
         -1 {c |}{res}          5        0.56        0.56
{txt}          1 {c |}{res}         64        7.17        7.74
{txt}          2 {c |}{res}        579       64.91       72.65
{txt}          3 {c |}{res}        237       26.57       99.22
{txt}          8 {c |}{res}          7        0.78      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        892      100.00

  {txt}RECODE of {c |}
Z_US92_E_Di {c |}
    sc3Know {c |}
(DISCUSS. 3 {c |}
  POLITICAL {c |}
 KNOWLEDGE) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
         -1 {c |}{res}          5        0.81        0.81
{txt}          1 {c |}{res}         61        9.85       10.66
{txt}          2 {c |}{res}        368       59.45       70.11
{txt}          3 {c |}{res}        180       29.08       99.19
{txt}          8 {c |}{res}          4        0.65       99.84
{txt}          9 {c |}{res}          1        0.16      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        619      100.00

  {txt}RECODE of {c |}
Z_US92_E_Di {c |}
    sc4Know {c |}
(DISCUSS. 4 {c |}
  POLITICAL {c |}
 KNOWLEDGE) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
         -1 {c |}{res}          5        1.25        1.25
{txt}          1 {c |}{res}         38        9.50       10.75
{txt}          2 {c |}{res}        250       62.50       73.25
{txt}          3 {c |}{res}        104       26.00       99.25
{txt}          8 {c |}{res}          3        0.75      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        400      100.00

  {txt}RECODE of {c |}
Z_US92_E_Di {c |}
    sc5Know {c |}
(DISCUSS. 5 {c |}
  POLITICAL {c |}
 KNOWLEDGE) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
         -1 {c |}{res}          5        0.63        0.63
{txt}          1 {c |}{res}         57        7.19        7.82
{txt}          2 {c |}{res}        466       58.76       66.58
{txt}          3 {c |}{res}        262       33.04       99.62
{txt}          8 {c |}{res}          2        0.25       99.87
{txt}         95 {c |}{res}          1        0.13      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        793      100.00
{txt}
{com}.         mvdecode disc1_knowl disc2_knowl disc3_knowl disc4_knowl disc5_knowl, mv(-1 = . \ 8 = . \ 9 = . \ 95 = . )
 {txt}disc1_knowl:{res}{col 15}15{txt} missing values generated
 disc2_knowl:{res}{col 15}12{txt} missing values generated
 disc3_knowl:{res}{col 15}10{txt} missing values generated
 disc4_knowl:{res}{col 15}8{txt} missing values generated
 disc5_knowl:{res}{col 15}8{txt} missing values generated

{com}.         label def kno 1 "Not Much" 2 "Avg." 3 "Great Deal"
{txt}
{com}.         foreach var in disc1_knowl disc2_knowl disc3_knowl disc4_knowl disc5_knowl {c -(}
{txt}  2{com}.                 label values `var' kno
{txt}  3{com}.                 {c )-}
{txt}
{com}. 
.         egen disc_knowl = rowmean(disc1_knowl disc2_knowl disc3_knowl disc4_knowl disc5_knowl)
{txt}(122 missing values generated)

{com}.         label var disc_knowl "Network Pol. Knowl."
{txt}
{com}.         summ disc_knowl

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 2}disc_knowl {c |}{res}      1201    2.204385    .4089776          1          3
{txt}
{com}.         
.         summ disc_knowl 

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 2}disc_knowl {c |}{res}      1201    2.204385    .4089776          1          3
{txt}
{com}.         gen disc_knowl01 = (disc_knowl - r(min))/(r(max)-r(min))
{txt}(122 missing values generated)

{com}.         label var disc_knowl01 "Network Pol. Knowledge"
{txt}
{com}.         
. 
.         
.                 *Weighted Scale of Exposure to Disagreement
.                         *Agree/Weight
.                                 gen a1k = d1_agree * disc1_knowl
{txt}(544 missing values generated)

{com}.                                 gen a2k = d2_agree * disc2_knowl
{txt}(685 missing values generated)

{com}.                                 gen a3k = d3_agree * disc3_knowl
{txt}(885 missing values generated)

{com}.                                 gen a4k = d4_agree * disc4_knowl
{txt}(1037 missing values generated)

{com}.                                 gen a5k = d5_agree * disc5_knowl
{txt}(741 missing values generated)

{com}.                                 egen agree_knowl = rowtotal(a1k a2k a3k a4k a5k), missing
{txt}(413 missing values generated)

{com}.                         *Disagree/Weight
.                                 gen d1k = d1_dagree * disc1_knowl
{txt}(544 missing values generated)

{com}.                                 gen d2k = d1_dagree * disc2_knowl
{txt}(670 missing values generated)

{com}.                                 gen d3k = d1_dagree * disc3_knowl
{txt}(857 missing values generated)

{com}.                                 gen d4k = d1_dagree * disc4_knowl
{txt}(1019 missing values generated)

{com}.                                 gen d5k = d1_dagree * disc5_knowl
{txt}(827 missing values generated)

{com}.                                 egen dagree_knowl = rowtotal(d1k d2k d3k d4k d5k), missing
{txt}(543 missing values generated)

{com}.                         *Scale
.                                 gen disagree_total_knowl = dagree_knowl - agree_knowl
{txt}(543 missing values generated)

{com}.                                 gen disagree_avg_knowl = disagree_total_knowl/(disagree+agree)
{txt}(543 missing values generated)

{com}.                                 label var disagree_total_knowl "Network Disagreement (Knowledge Weighted)"
{txt}
{com}.                                 label var disagree_avg_knowl "Network Disagreement (Knowledge Weighted)"
{txt}
{com}.                                 
.                                 foreach var in disagree_total_knowl disagree_avg_knowl {c -(}
{txt}  2{com}.                                         summ `var'
{txt}  3{com}.                                         gen `var'01 = (`var' - r(min))/(r(max)-r(min))
{txt}  4{com}.                                         {c )-}

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
disa~l_knowl {c |}{res}       780   -2.155128    5.857336        -15         13
{txt}(543 missing values generated)

    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
disagree_a~l {c |}{res}       780   -.6894658    1.824984         -3          8
{txt}(543 missing values generated)

{com}.                                 
.                                 label var disagree_total_knowl01 "Network Disagreement (Knowledge Weighted)"
{txt}
{com}.                                 label var disagree_avg_knowl01 "Network Disagreement (Knowledge Weighted)"
{txt}
{com}.                                 
.         
. *Frequency of Political Discussion*
.         foreach var in Z_US92_E_Disc1Freq Z_US92_E_Disc2Freq    Z_US92_E_Disc3Freq Z_US92_E_Disc4Freq ///
>                 Z_US92_E_Disc5Freq {c -(}
{txt}  2{com}.                 codebook `var'
{txt}  3{com}.                 {c )-}

{txt}{hline}
{res}Z_US92_E_Disc1Freq{right:FREQ TALK POLITICS, DISCUSS. 1}
{txt}{hline}

{col 19}type:  numeric ({res}byte{txt})
{ralign 22:label}:  {res:V520_A}, but {res:1} nonmissing value is not labeled

{col 18}range:  [{res}-1{txt},{res}9{txt}]{col 55}units:  {res}1
{col 10}{txt}unique values:  {res}6{col 51}{txt}missing .:  {res}250{txt}/{res}1323

{txt}{col 13}tabulation:  Freq.   Numeric  Label
{col 24}{res}      5{col 33}      -1{col 43}
{col 24}    278{col 33}       1{col 43}{txt}often
{col 24}{res}    556{col 33}       2{col 43}{txt}sometimes
{col 24}{res}    194{col 33}       3{col 43}{txt}rarely
{col 24}{res}     38{col 33}       4{col 43}{txt}never
{col 24}{res}      2{col 33}       9{col 43}{txt}refused
{col 24}{res}    250{col 33}       .{col 43}

{txt}{hline}
{res}Z_US92_E_Disc2Freq{right:FREQ TALK POLITICS, DISCUSS. 2}
{txt}{hline}

{col 19}type:  numeric ({res}byte{txt})
{ralign 22:label}:  {res:LABAD}, but {res:1} nonmissing value is not labeled

{col 18}range:  [{res}-1{txt},{res}8{txt}]{col 55}units:  {res}1
{col 10}{txt}unique values:  {res}6{col 51}{txt}missing .:  {res}431{txt}/{res}1323

{txt}{col 13}tabulation:  Freq.   Numeric  Label
{col 24}{res}      5{col 33}      -1{col 43}
{col 24}    147{col 33}       1{col 43}{txt}often
{col 24}{res}    467{col 33}       2{col 43}{txt}sometimes
{col 24}{res}    231{col 33}       3{col 43}{txt}rarely
{col 24}{res}     41{col 33}       4{col 43}{txt}never
{col 24}{res}      1{col 33}       8{col 43}{txt}dk
{col 24}{res}    431{col 33}       .{col 43}

{txt}{hline}
{res}Z_US92_E_Disc3Freq{right:FREQ TALK POLITICS, DISCUSS. 3}
{txt}{hline}

{col 19}type:  numeric ({res}byte{txt})
{ralign 22:label}:  {res:LABAD}, but {res:1} nonmissing value is not labeled

{col 18}range:  [{res}-1{txt},{res}9{txt}]{col 55}units:  {res}1
{col 10}{txt}unique values:  {res}6{col 51}{txt}missing .:  {res}704{txt}/{res}1323

{txt}{col 13}tabulation:  Freq.   Numeric  Label
{col 24}{res}      5{col 33}      -1{col 43}
{col 24}    106{col 33}       1{col 43}{txt}often
{col 24}{res}    278{col 33}       2{col 43}{txt}sometimes
{col 24}{res}    195{col 33}       3{col 43}{txt}rarely
{col 24}{res}     34{col 33}       4{col 43}{txt}never
{col 24}{res}      1{col 33}       9{col 43}{txt}refused
{col 24}{res}    704{col 33}       .{col 43}

{txt}{hline}
{res}Z_US92_E_Disc4Freq{right:FREQ TALK POLITICS, DISCUSS. 4}
{txt}{hline}

{col 19}type:  numeric ({res}byte{txt})
{ralign 22:label}:  {res:LABAM}, but {res:1} nonmissing value is not labeled

{col 18}range:  [{res}-1{txt},{res}4{txt}]{col 55}units:  {res}1
{col 10}{txt}unique values:  {res}5{col 51}{txt}missing .:  {res}923{txt}/{res}1323

{txt}{col 13}tabulation:  Freq.   Numeric  Label
{col 24}{res}      5{col 33}      -1{col 43}
{col 24}     60{col 33}       1{col 43}{txt}often
{col 24}{res}    184{col 33}       2{col 43}{txt}sometimes
{col 24}{res}    128{col 33}       3{col 43}{txt}rarely
{col 24}{res}     23{col 33}       4{col 43}{txt}never
{col 24}{res}    923{col 33}       .{col 43}

{txt}{hline}
{res}Z_US92_E_Disc5Freq{right:FREQ TALK POLITICS, DISCUSS. 5}
{txt}{hline}

{col 19}type:  numeric ({res}byte{txt})
{ralign 22:label}:  {res:LABAM}, but {res:1} nonmissing value is not labeled

{col 18}range:  [{res}-1{txt},{res}95{txt}]{col 55}units:  {res}1
{col 10}{txt}unique values:  {res}6{col 51}{txt}missing .:  {res}530{txt}/{res}1323

{txt}{col 13}tabulation:  Freq.   Numeric  Label
{col 24}{res}      5{col 33}      -1{col 43}
{col 24}    199{col 33}       1{col 43}{txt}often
{col 24}{res}    422{col 33}       2{col 43}{txt}sometimes
{col 24}{res}    153{col 33}       3{col 43}{txt}rarely
{col 24}{res}     13{col 33}       4{col 43}{txt}never
{col 24}{res}      1{col 33}      95{col 43}{txt}inap
{col 24}{res}    530{col 33}       .{col 43}
{txt}
{com}.                 
.         recode Z_US92_E_Disc1Freq (1=4) (2=3) (3=2) (4=1), gen(disc1_freq)
{txt}(1066 differences between Z_US92_E_Disc1Freq and disc1_freq)

{com}.         recode Z_US92_E_Disc2Freq (1=4) (2=3) (3=2) (4=1), gen(disc2_freq)
{txt}(886 differences between Z_US92_E_Disc2Freq and disc2_freq)

{com}.         recode Z_US92_E_Disc3Freq (1=4) (2=3) (3=2) (4=1), gen(disc3_freq)
{txt}(613 differences between Z_US92_E_Disc3Freq and disc3_freq)

{com}.         recode Z_US92_E_Disc4Freq (1=4) (2=3) (3=2) (4=1), gen(disc4_freq)
{txt}(395 differences between Z_US92_E_Disc4Freq and disc4_freq)

{com}.         recode Z_US92_E_Disc5Freq (1=4) (2=3) (3=2) (4=1), gen(disc5_freq)
{txt}(787 differences between Z_US92_E_Disc5Freq and disc5_freq)

{com}.         foreach var in disc1_freq disc2_freq disc3_freq disc4_freq disc5_freq {c -(}
{txt}  2{com}.                 tab `var'
{txt}  3{com}.                 {c )-}

  {txt}RECODE of {c |}
Z_US92_E_Di {c |}
    sc1Freq {c |}
 (FREQ TALK {c |}
  POLITICS, {c |}
DISCUSS. 1) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
         -1 {c |}{res}          5        0.47        0.47
{txt}          1 {c |}{res}         38        3.54        4.01
{txt}          2 {c |}{res}        194       18.08       22.09
{txt}          3 {c |}{res}        556       51.82       73.90
{txt}          4 {c |}{res}        278       25.91       99.81
{txt}          9 {c |}{res}          2        0.19      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,073      100.00

  {txt}RECODE of {c |}
Z_US92_E_Di {c |}
    sc2Freq {c |}
 (FREQ TALK {c |}
  POLITICS, {c |}
DISCUSS. 2) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
         -1 {c |}{res}          5        0.56        0.56
{txt}          1 {c |}{res}         41        4.60        5.16
{txt}          2 {c |}{res}        231       25.90       31.05
{txt}          3 {c |}{res}        467       52.35       83.41
{txt}          4 {c |}{res}        147       16.48       99.89
{txt}          8 {c |}{res}          1        0.11      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        892      100.00

  {txt}RECODE of {c |}
Z_US92_E_Di {c |}
    sc3Freq {c |}
 (FREQ TALK {c |}
  POLITICS, {c |}
DISCUSS. 3) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
         -1 {c |}{res}          5        0.81        0.81
{txt}          1 {c |}{res}         34        5.49        6.30
{txt}          2 {c |}{res}        195       31.50       37.80
{txt}          3 {c |}{res}        278       44.91       82.71
{txt}          4 {c |}{res}        106       17.12       99.84
{txt}          9 {c |}{res}          1        0.16      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        619      100.00

  {txt}RECODE of {c |}
Z_US92_E_Di {c |}
    sc4Freq {c |}
 (FREQ TALK {c |}
  POLITICS, {c |}
DISCUSS. 4) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
         -1 {c |}{res}          5        1.25        1.25
{txt}          1 {c |}{res}         23        5.75        7.00
{txt}          2 {c |}{res}        128       32.00       39.00
{txt}          3 {c |}{res}        184       46.00       85.00
{txt}          4 {c |}{res}         60       15.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        400      100.00

  {txt}RECODE of {c |}
Z_US92_E_Di {c |}
    sc5Freq {c |}
 (FREQ TALK {c |}
  POLITICS, {c |}
DISCUSS. 5) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
         -1 {c |}{res}          5        0.63        0.63
{txt}          1 {c |}{res}         13        1.64        2.27
{txt}          2 {c |}{res}        153       19.29       21.56
{txt}          3 {c |}{res}        422       53.22       74.78
{txt}          4 {c |}{res}        199       25.09       99.87
{txt}         95 {c |}{res}          1        0.13      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        793      100.00
{txt}
{com}.         
.         mvdecode disc1_freq disc2_freq disc3_freq disc4_freq disc5_freq, mv(-1 = . \ 8 = . \ 9 = . \ 95 = . )
  {txt}disc1_freq:{res}{col 15}7{txt} missing values generated
  disc2_freq:{res}{col 15}6{txt} missing values generated
  disc3_freq:{res}{col 15}6{txt} missing values generated
  disc4_freq:{res}{col 15}5{txt} missing values generated
  disc5_freq:{res}{col 15}6{txt} missing values generated

{com}.         
.         egen disc_freq = rowmean(disc1_freq disc2_freq disc3_freq disc4_freq disc5_freq)
{txt}(121 missing values generated)

{com}.         label var disc_freq "Avg. Pol Discussion Freq"
{txt}
{com}.         summ disc_freq 

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 3}disc_freq {c |}{res}      1202    2.883999    .5892596          1          4
{txt}
{com}. 
.                 *Weighted Scale of Exposure to Disagreement
.                         *Agree/Weight
.                                 gen a1f = d1_agree * disc1_freq
{txt}(543 missing values generated)

{com}.                                 gen a2f = d2_agree * disc2_freq
{txt}(684 missing values generated)

{com}.                                 gen a3f = d3_agree * disc3_freq
{txt}(884 missing values generated)

{com}.                                 gen a4f = d4_agree * disc4_freq
{txt}(1036 missing values generated)

{com}.                                 gen a5f = d5_agree * disc5_freq
{txt}(741 missing values generated)

{com}.                                 egen agree_freq = rowtotal(a1f a2f a3f a4f a5f), missing
{txt}(413 missing values generated)

{com}.                         *Disagree/Weight
.                                 gen d1f = d1_dagree * disc1_freq
{txt}(543 missing values generated)

{com}.                                 gen d2f = d1_dagree * disc2_freq
{txt}(666 missing values generated)

{com}.                                 gen d3f = d1_dagree * disc3_freq
{txt}(855 missing values generated)

{com}.                                 gen d4f = d1_dagree * disc4_freq
{txt}(1016 missing values generated)

{com}.                                 gen d5f = d1_dagree * disc5_freq
{txt}(826 missing values generated)

{com}.                                 egen dagree_freq = rowtotal(d1f d2f d3f d4f d5f), missing
{txt}(543 missing values generated)

{com}.                         *Scale
.                                 gen disagree_total_freq = dagree_freq - agree_freq
{txt}(543 missing values generated)

{com}.                                 gen disagree_avg_freq = disagree_total_freq/(disagree+agree)
{txt}(543 missing values generated)

{com}.                                 label var disagree_total_freq "Network Disagreement (Frequency Weighted)"
{txt}
{com}.                                 label var disagree_avg_freq "Network Disagreement (Frequency Weighted)"
{txt}
{com}.                                 
.                                 foreach var in disagree_total_freq disagree_avg_freq {c -(}
{txt}  2{com}.                                         summ `var'
{txt}  3{com}.                                         gen `var'01 = (`var' - r(min))/(r(max)-r(min))
{txt}  4{com}.                                         {c )-}

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
disagree_t~q {c |}{res}       780   -2.855128    7.781898        -19         16
{txt}(543 missing values generated)

    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
disagree_a~q {c |}{res}       780   -.9056624    2.424981         -4         10
{txt}(543 missing values generated)

{com}.                                 
.                                 label var disagree_total_freq01 "Network Disagreement (Frequency Weighted)"
{txt}
{com}.                                 label var disagree_avg_freq01 "Network Disagreement (Frequency Weighted)"
{txt}
{com}.                                 
.                                 
. 
{txt}end of do-file

{com}. set more off
{txt}
{com}. 
. 
. /****Partisan Extremity****/
. 
. eststo clear
{txt}
{com}. eststo: ologit pid_str_full c.disagree_total numgiven disc_knowl i.pid_2 news_att i.gender i.race age educ ///
>                         income i.marital

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-834.24262}  
Iteration 1:{space 3}log likelihood = {res:-805.85452}  
Iteration 2:{space 3}log likelihood = {res:-805.77124}  
Iteration 3:{space 3}log likelihood = {res:-805.77123}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       801
{txt}{col 51}LR chi2({res}12{txt}){col 67}= {res}     56.94
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-805.77123{txt}{col 51}Pseudo R2{col 67}= {res}    0.0341

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}  pid_str_full{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
disagree_total {c |}{col 16}{res}{space 2}-.1614067{col 28}{space 2} .0352453{col 39}{space 1}   -4.58{col 48}{space 3}0.000{col 56}{space 4}-.2304863{col 69}{space 3}-.0923271
{txt}{space 6}numgiven {c |}{col 16}{res}{space 2} .0210508{col 28}{space 2} .0516685{col 39}{space 1}    0.41{col 48}{space 3}0.684{col 56}{space 4}-.0802177{col 69}{space 3} .1223193
{txt}{space 4}disc_knowl {c |}{col 16}{res}{space 2} .5574842{col 28}{space 2} .1853663{col 39}{space 1}    3.01{col 48}{space 3}0.003{col 56}{space 4}  .194173{col 69}{space 3} .9207954
{txt}{space 14} {c |}
{space 9}pid_2 {c |}
{space 5}Democrat  {c |}{col 16}{res}{space 2}-.2453091{col 28}{space 2} .1400758{col 39}{space 1}   -1.75{col 48}{space 3}0.080{col 56}{space 4}-.5198526{col 69}{space 3} .0292343
{txt}{space 6}news_att {c |}{col 16}{res}{space 2}-.0672561{col 28}{space 2} .0759777{col 39}{space 1}   -0.89{col 48}{space 3}0.376{col 56}{space 4}-.2161696{col 69}{space 3} .0816573
{txt}{space 14} {c |}
{space 8}gender {c |}
{space 7}Female  {c |}{col 16}{res}{space 2} .0583914{col 28}{space 2} .1386094{col 39}{space 1}    0.42{col 48}{space 3}0.674{col 56}{space 4}-.2132781{col 69}{space 3} .3300609
{txt}{space 14} {c |}
{space 10}race {c |}
{space 8}Black  {c |}{col 16}{res}{space 2} .6884068{col 28}{space 2} .2854743{col 39}{space 1}    2.41{col 48}{space 3}0.016{col 56}{space 4} .1288875{col 69}{space 3} 1.247926
{txt}{space 8}Other  {c |}{col 16}{res}{space 2} .2199916{col 28}{space 2}  .355602{col 39}{space 1}    0.62{col 48}{space 3}0.536{col 56}{space 4}-.4769755{col 69}{space 3} .9169587
{txt}{space 14} {c |}
{space 11}age {c |}{col 16}{res}{space 2} .0026862{col 28}{space 2} .0042702{col 39}{space 1}    0.63{col 48}{space 3}0.529{col 56}{space 4}-.0056833{col 69}{space 3} .0110557
{txt}{space 10}educ {c |}{col 16}{res}{space 2} .1775052{col 28}{space 2} .0812584{col 39}{space 1}    2.18{col 48}{space 3}0.029{col 56}{space 4} .0182417{col 69}{space 3} .3367687
{txt}{space 8}income {c |}{col 16}{res}{space 2} .0598826{col 28}{space 2} .0510943{col 39}{space 1}    1.17{col 48}{space 3}0.241{col 56}{space 4}-.0402603{col 69}{space 3} .1600255
{txt}{space 14} {c |}
{space 7}marital {c |}
{space 6}Married  {c |}{col 16}{res}{space 2}-.0371879{col 28}{space 2} .1518778{col 39}{space 1}   -0.24{col 48}{space 3}0.807{col 56}{space 4}-.3348629{col 69}{space 3} .2604871
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
         /cut1 {c |}{col 16}{res}{space 2} 1.400702{col 28}{space 2} .5239989{col 56}{space 4}  .373683{col 69}{space 3} 2.427721
{txt}         /cut2 {c |}{col 16}{res}{space 2} 2.343717{col 28}{space 2} .5282071{col 56}{space 4}  1.30845{col 69}{space 3} 3.378984
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{com}. eststo: ologit pid_str_full c.disagree_avg numgiven disc_knowl i.pid_2 news_att i.gender i.race age educ ///
>                 income i.marital

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-834.24262}  
Iteration 1:{space 3}log likelihood = {res:-809.43976}  
Iteration 2:{space 3}log likelihood = {res:-809.38668}  
Iteration 3:{space 3}log likelihood = {res:-809.38668}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       801
{txt}{col 51}LR chi2({res}12{txt}){col 67}= {res}     49.71
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-809.38668{txt}{col 51}Pseudo R2{col 67}= {res}    0.0298

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}pid_str_full{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
disagree_avg {c |}{col 14}{res}{space 2}-.3892371{col 26}{space 2} .1039653{col 37}{space 1}   -3.74{col 46}{space 3}0.000{col 54}{space 4}-.5930055{col 67}{space 3}-.1854688
{txt}{space 4}numgiven {c |}{col 14}{res}{space 2} .0716344{col 26}{space 2} .0513946{col 37}{space 1}    1.39{col 46}{space 3}0.163{col 54}{space 4}-.0290971{col 67}{space 3}  .172366
{txt}{space 2}disc_knowl {c |}{col 14}{res}{space 2}  .585043{col 26}{space 2} .1850718{col 37}{space 1}    3.16{col 46}{space 3}0.002{col 54}{space 4} .2223089{col 67}{space 3}  .947777
{txt}{space 12} {c |}
{space 7}pid_2 {c |}
{space 3}Democrat  {c |}{col 14}{res}{space 2}-.2279173{col 26}{space 2}  .139657{col 37}{space 1}   -1.63{col 46}{space 3}0.103{col 54}{space 4}  -.50164{col 67}{space 3} .0458054
{txt}{space 4}news_att {c |}{col 14}{res}{space 2}-.0607923{col 26}{space 2} .0757055{col 37}{space 1}   -0.80{col 46}{space 3}0.422{col 54}{space 4}-.2091723{col 67}{space 3} .0875877
{txt}{space 12} {c |}
{space 6}gender {c |}
{space 5}Female  {c |}{col 14}{res}{space 2} .0846879{col 26}{space 2} .1382556{col 37}{space 1}    0.61{col 46}{space 3}0.540{col 54}{space 4}-.1862881{col 67}{space 3} .3556638
{txt}{space 12} {c |}
{space 8}race {c |}
{space 6}Black  {c |}{col 14}{res}{space 2} .6552777{col 26}{space 2} .2848083{col 37}{space 1}    2.30{col 46}{space 3}0.021{col 54}{space 4} .0970636{col 67}{space 3} 1.213492
{txt}{space 6}Other  {c |}{col 14}{res}{space 2} .2318601{col 26}{space 2} .3554697{col 37}{space 1}    0.65{col 46}{space 3}0.514{col 54}{space 4}-.4648476{col 67}{space 3} .9285679
{txt}{space 12} {c |}
{space 9}age {c |}{col 14}{res}{space 2}  .002463{col 26}{space 2} .0042749{col 37}{space 1}    0.58{col 46}{space 3}0.565{col 54}{space 4}-.0059156{col 67}{space 3} .0108415
{txt}{space 8}educ {c |}{col 14}{res}{space 2} .1634638{col 26}{space 2} .0808801{col 37}{space 1}    2.02{col 46}{space 3}0.043{col 54}{space 4} .0049417{col 67}{space 3} .3219858
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0637336{col 26}{space 2} .0509579{col 37}{space 1}    1.25{col 46}{space 3}0.211{col 54}{space 4}-.0361421{col 67}{space 3} .1636092
{txt}{space 12} {c |}
{space 5}marital {c |}
{space 4}Married  {c |}{col 14}{res}{space 2}-.0515236{col 26}{space 2} .1520976{col 37}{space 1}   -0.34{col 46}{space 3}0.735{col 54}{space 4}-.3496294{col 67}{space 3} .2465823
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
       /cut1 {c |}{col 14}{res}{space 2}  1.59441{col 26}{space 2} .5233412{col 54}{space 4} .5686798{col 67}{space 3} 2.620139
{txt}       /cut2 {c |}{col 14}{res}{space 2} 2.530715{col 26}{space 2} .5279717{col 54}{space 4}  1.49591{col 67}{space 3} 3.565521
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{com}.                         
. eststo: ologit pid_str_full c.disagree_total_freq numgiven disc_knowl i.pid_2 news_att i.gender i.race age educ ///
>                 income i.marital

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-716.48568}  
Iteration 1:{space 3}log likelihood = {res:-695.11703}  
Iteration 2:{space 3}log likelihood = {res:-695.05189}  
Iteration 3:{space 3}log likelihood = {res:-695.05188}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       691
{txt}{col 51}LR chi2({res}12{txt}){col 67}= {res}     42.87
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-695.05188{txt}{col 51}Pseudo R2{col 67}= {res}    0.0299

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}       pid_str_full{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
disagree_total_freq {c |}{col 21}{res}{space 2}-.0325627{col 33}{space 2} .0097149{col 44}{space 1}   -3.35{col 53}{space 3}0.001{col 61}{space 4}-.0516035{col 74}{space 3}-.0135218
{txt}{space 11}numgiven {c |}{col 21}{res}{space 2} .0695243{col 33}{space 2} .0589752{col 44}{space 1}    1.18{col 53}{space 3}0.238{col 61}{space 4} -.046065{col 74}{space 3} .1851137
{txt}{space 9}disc_knowl {c |}{col 21}{res}{space 2} .7126469{col 33}{space 2} .2115804{col 44}{space 1}    3.37{col 53}{space 3}0.001{col 61}{space 4}  .297957{col 74}{space 3} 1.127337
{txt}{space 19} {c |}
{space 14}pid_2 {c |}
{space 10}Democrat  {c |}{col 21}{res}{space 2}-.1678639{col 33}{space 2} .1512085{col 44}{space 1}   -1.11{col 53}{space 3}0.267{col 61}{space 4}-.4642271{col 74}{space 3} .1284993
{txt}{space 11}news_att {c |}{col 21}{res}{space 2}-.1071112{col 33}{space 2} .0824769{col 44}{space 1}   -1.30{col 53}{space 3}0.194{col 61}{space 4} -.268763{col 74}{space 3} .0545406
{txt}{space 19} {c |}
{space 13}gender {c |}
{space 12}Female  {c |}{col 21}{res}{space 2} .1028881{col 33}{space 2} .1497696{col 44}{space 1}    0.69{col 53}{space 3}0.492{col 61}{space 4}-.1906549{col 74}{space 3} .3964311
{txt}{space 19} {c |}
{space 15}race {c |}
{space 13}Black  {c |}{col 21}{res}{space 2} .6914301{col 33}{space 2} .3110976{col 44}{space 1}    2.22{col 53}{space 3}0.026{col 61}{space 4}   .08169{col 74}{space 3}  1.30117
{txt}{space 13}Other  {c |}{col 21}{res}{space 2}-.0471025{col 33}{space 2}  .385871{col 44}{space 1}   -0.12{col 53}{space 3}0.903{col 61}{space 4}-.8033957{col 74}{space 3} .7091907
{txt}{space 19} {c |}
{space 16}age {c |}{col 21}{res}{space 2} .0028848{col 33}{space 2} .0047361{col 44}{space 1}    0.61{col 53}{space 3}0.542{col 61}{space 4}-.0063979{col 74}{space 3} .0121674
{txt}{space 15}educ {c |}{col 21}{res}{space 2} .1255484{col 33}{space 2} .0881737{col 44}{space 1}    1.42{col 53}{space 3}0.154{col 61}{space 4} -.047269{col 74}{space 3} .2983657
{txt}{space 13}income {c |}{col 21}{res}{space 2} .0557305{col 33}{space 2} .0549268{col 44}{space 1}    1.01{col 53}{space 3}0.310{col 61}{space 4}-.0519241{col 74}{space 3} .1633851
{txt}{space 19} {c |}
{space 12}marital {c |}
{space 11}Married  {c |}{col 21}{res}{space 2}-.0752742{col 33}{space 2} .1632638{col 44}{space 1}   -0.46{col 53}{space 3}0.645{col 61}{space 4}-.3952653{col 74}{space 3} .2447169
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
              /cut1 {c |}{col 21}{res}{space 2} 1.691703{col 33}{space 2} .5927274{col 61}{space 4} .5299784{col 74}{space 3} 2.853427
{txt}              /cut2 {c |}{col 21}{res}{space 2} 2.644299{col 33}{space 2} .5976402{col 61}{space 4} 1.472946{col 74}{space 3} 3.815653
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{com}.                         
. eststo: ologit pid_str_full c.disagree_total_knowl numgiven disc_knowl i.pid_2 news_att i.gender i.race age educ ///
>                         income i.marital

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-716.48568}  
Iteration 1:{space 3}log likelihood = {res: -695.3798}  
Iteration 2:{space 3}log likelihood = {res:-695.31101}  
Iteration 3:{space 3}log likelihood = {res:  -695.311}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       691
{txt}{col 51}LR chi2({res}12{txt}){col 67}= {res}     42.35
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}  -695.311{txt}{col 51}Pseudo R2{col 67}= {res}    0.0296

{txt}{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}        pid_str_full{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
disagree_total_knowl {c |}{col 22}{res}{space 2}-.0422858{col 34}{space 2} .0129013{col 45}{space 1}   -3.28{col 54}{space 3}0.001{col 62}{space 4}-.0675719{col 75}{space 3}-.0169997
{txt}{space 12}numgiven {c |}{col 22}{res}{space 2} .0708775{col 34}{space 2} .0589219{col 45}{space 1}    1.20{col 54}{space 3}0.229{col 62}{space 4}-.0446073{col 75}{space 3} .1863622
{txt}{space 10}disc_knowl {c |}{col 22}{res}{space 2} .6894737{col 34}{space 2} .2122549{col 45}{space 1}    3.25{col 54}{space 3}0.001{col 62}{space 4} .2734619{col 75}{space 3} 1.105486
{txt}{space 20} {c |}
{space 15}pid_2 {c |}
{space 11}Democrat  {c |}{col 22}{res}{space 2}-.1651807{col 34}{space 2} .1511391{col 45}{space 1}   -1.09{col 54}{space 3}0.274{col 62}{space 4}-.4614079{col 75}{space 3} .1310465
{txt}{space 12}news_att {c |}{col 22}{res}{space 2}-.1039212{col 34}{space 2} .0825052{col 45}{space 1}   -1.26{col 54}{space 3}0.208{col 62}{space 4}-.2656284{col 75}{space 3} .0577859
{txt}{space 20} {c |}
{space 14}gender {c |}
{space 13}Female  {c |}{col 22}{res}{space 2} .0986034{col 34}{space 2} .1497221{col 45}{space 1}    0.66{col 54}{space 3}0.510{col 62}{space 4}-.1948465{col 75}{space 3} .3920534
{txt}{space 20} {c |}
{space 16}race {c |}
{space 14}Black  {c |}{col 22}{res}{space 2} .7002966{col 34}{space 2} .3110784{col 45}{space 1}    2.25{col 54}{space 3}0.024{col 62}{space 4} .0905942{col 75}{space 3} 1.309999
{txt}{space 14}Other  {c |}{col 22}{res}{space 2}-.0556195{col 34}{space 2} .3856269{col 45}{space 1}   -0.14{col 54}{space 3}0.885{col 62}{space 4}-.8114343{col 75}{space 3} .7001954
{txt}{space 20} {c |}
{space 17}age {c |}{col 22}{res}{space 2} .0032293{col 34}{space 2} .0047293{col 45}{space 1}    0.68{col 54}{space 3}0.495{col 62}{space 4}  -.00604{col 75}{space 3} .0124986
{txt}{space 16}educ {c |}{col 22}{res}{space 2} .1305251{col 34}{space 2} .0882403{col 45}{space 1}    1.48{col 54}{space 3}0.139{col 62}{space 4}-.0424227{col 75}{space 3} .3034728
{txt}{space 14}income {c |}{col 22}{res}{space 2} .0561836{col 34}{space 2} .0549003{col 45}{space 1}    1.02{col 54}{space 3}0.306{col 62}{space 4} -.051419{col 75}{space 3} .1637862
{txt}{space 20} {c |}
{space 13}marital {c |}
{space 12}Married  {c |}{col 22}{res}{space 2}-.0760894{col 34}{space 2} .1632186{col 45}{space 1}   -0.47{col 54}{space 3}0.641{col 62}{space 4}-.3959919{col 75}{space 3} .2438132
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
               /cut1 {c |}{col 22}{res}{space 2} 1.672593{col 34}{space 2} .5931875{col 62}{space 4} .5099668{col 75}{space 3} 2.835219
{txt}               /cut2 {c |}{col 22}{res}{space 2} 2.624557{col 34}{space 2} .5980648{col 62}{space 4} 1.452372{col 75}{space 3} 3.796743
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{com}. 
. eststo: ologit pid_str_full c.disagree_total_weight numgiven disc_knowl i.pid_2 news_att i.gender i.race age educ ///
>                         income i.marital

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-829.94188}  
Iteration 1:{space 3}log likelihood = {res:-799.59287}  
Iteration 2:{space 3}log likelihood = {res:-799.49697}  
Iteration 3:{space 3}log likelihood = {res:-799.49696}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       797
{txt}{col 51}LR chi2({res}12{txt}){col 67}= {res}     60.89
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-799.49696{txt}{col 51}Pseudo R2{col 67}= {res}    0.0367

{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}         pid_str_full{col 23}{c |}      Coef.{col 35}   Std. Err.{col 47}      z{col 55}   P>|z|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
disagree_total_weight {c |}{col 23}{res}{space 2}-.0698463{col 35}{space 2}  .013478{col 46}{space 1}   -5.18{col 55}{space 3}0.000{col 63}{space 4}-.0962628{col 76}{space 3}-.0434298
{txt}{space 13}numgiven {c |}{col 23}{res}{space 2} .0213969{col 35}{space 2} .0517414{col 46}{space 1}    0.41{col 55}{space 3}0.679{col 63}{space 4}-.0800144{col 76}{space 3} .1228083
{txt}{space 11}disc_knowl {c |}{col 23}{res}{space 2} .5222195{col 35}{space 2} .1866388{col 46}{space 1}    2.80{col 55}{space 3}0.005{col 63}{space 4} .1564141{col 76}{space 3} .8880249
{txt}{space 21} {c |}
{space 16}pid_2 {c |}
{space 12}Democrat  {c |}{col 23}{res}{space 2}-.2119948{col 35}{space 2} .1404719{col 46}{space 1}   -1.51{col 55}{space 3}0.131{col 63}{space 4}-.4873147{col 76}{space 3} .0633251
{txt}{space 13}news_att {c |}{col 23}{res}{space 2}-.0612544{col 35}{space 2} .0762023{col 46}{space 1}   -0.80{col 55}{space 3}0.421{col 63}{space 4}-.2106081{col 76}{space 3} .0880994
{txt}{space 21} {c |}
{space 15}gender {c |}
{space 14}Female  {c |}{col 23}{res}{space 2} .0555535{col 35}{space 2} .1392456{col 46}{space 1}    0.40{col 55}{space 3}0.690{col 63}{space 4}-.2173629{col 76}{space 3} .3284698
{txt}{space 21} {c |}
{space 17}race {c |}
{space 15}Black  {c |}{col 23}{res}{space 2} .6573283{col 35}{space 2} .2862721{col 46}{space 1}    2.30{col 55}{space 3}0.022{col 63}{space 4} .0962453{col 76}{space 3} 1.218411
{txt}{space 15}Other  {c |}{col 23}{res}{space 2} .2268457{col 35}{space 2} .3557693{col 46}{space 1}    0.64{col 55}{space 3}0.524{col 63}{space 4}-.4704493{col 76}{space 3} .9241408
{txt}{space 21} {c |}
{space 18}age {c |}{col 23}{res}{space 2} .0034243{col 35}{space 2} .0042973{col 46}{space 1}    0.80{col 55}{space 3}0.426{col 63}{space 4}-.0049983{col 76}{space 3} .0118469
{txt}{space 17}educ {c |}{col 23}{res}{space 2} .1781613{col 35}{space 2} .0815347{col 46}{space 1}    2.19{col 55}{space 3}0.029{col 63}{space 4} .0183562{col 76}{space 3} .3379664
{txt}{space 15}income {c |}{col 23}{res}{space 2} .0663022{col 35}{space 2} .0513481{col 46}{space 1}    1.29{col 55}{space 3}0.197{col 63}{space 4}-.0343382{col 76}{space 3} .1669426
{txt}{space 21} {c |}
{space 14}marital {c |}
{space 13}Married  {c |}{col 23}{res}{space 2}-.0438117{col 35}{space 2} .1525009{col 46}{space 1}   -0.29{col 55}{space 3}0.774{col 63}{space 4} -.342708{col 76}{space 3} .2550846
{txt}{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                /cut1 {c |}{col 23}{res}{space 2} 1.364176{col 35}{space 2} .5283808{col 63}{space 4} .3285689{col 76}{space 3} 2.399783
{txt}                /cut2 {c |}{col 23}{res}{space 2} 2.317016{col 35}{space 2}  .532481{col 63}{space 4} 1.273373{col 76}{space 3}  3.36066
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est5{txt} stored)

{com}. 
. eststo: ologit pid_str_full c.genavg numgiven disc_knowl i.pid_2 news_att i.gender i.race age educ ///
>                         income i.marital

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-1057.3528}  
Iteration 1:{space 3}log likelihood = {res:-1034.4504}  
Iteration 2:{space 3}log likelihood = {res:-1034.4219}  
Iteration 3:{space 3}log likelihood = {res:-1034.4219}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}      1009
{txt}{col 51}LR chi2({res}12{txt}){col 67}= {res}     45.86
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-1034.4219{txt}{col 51}Pseudo R2{col 67}= {res}    0.0217

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}pid_str_full{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 6}genavg {c |}{col 14}{res}{space 2}-.2674623{col 26}{space 2} .1060415{col 37}{space 1}   -2.52{col 46}{space 3}0.012{col 54}{space 4}-.4752998{col 67}{space 3}-.0596249
{txt}{space 4}numgiven {c |}{col 14}{res}{space 2} .0749619{col 26}{space 2} .0447995{col 37}{space 1}    1.67{col 46}{space 3}0.094{col 54}{space 4}-.0128435{col 67}{space 3} .1627673
{txt}{space 2}disc_knowl {c |}{col 14}{res}{space 2}  .451276{col 26}{space 2} .1617906{col 37}{space 1}    2.79{col 46}{space 3}0.005{col 54}{space 4} .1341722{col 67}{space 3} .7683798
{txt}{space 12} {c |}
{space 7}pid_2 {c |}
{space 3}Democrat  {c |}{col 14}{res}{space 2}-.1688212{col 26}{space 2}  .123666{col 37}{space 1}   -1.37{col 46}{space 3}0.172{col 54}{space 4}-.4112022{col 67}{space 3} .0735597
{txt}{space 4}news_att {c |}{col 14}{res}{space 2} .0501407{col 26}{space 2} .0661659{col 37}{space 1}    0.76{col 46}{space 3}0.449{col 54}{space 4}-.0795421{col 67}{space 3} .1798235
{txt}{space 12} {c |}
{space 6}gender {c |}
{space 5}Female  {c |}{col 14}{res}{space 2}-.0151829{col 26}{space 2} .1230855{col 37}{space 1}   -0.12{col 46}{space 3}0.902{col 54}{space 4} -.256426{col 67}{space 3} .2260602
{txt}{space 12} {c |}
{space 8}race {c |}
{space 6}Black  {c |}{col 14}{res}{space 2} .6757771{col 26}{space 2} .2423037{col 37}{space 1}    2.79{col 46}{space 3}0.005{col 54}{space 4} .2008706{col 67}{space 3} 1.150684
{txt}{space 6}Other  {c |}{col 14}{res}{space 2} .2051094{col 26}{space 2} .2927948{col 37}{space 1}    0.70{col 46}{space 3}0.484{col 54}{space 4}-.3687579{col 67}{space 3} .7789768
{txt}{space 12} {c |}
{space 9}age {c |}{col 14}{res}{space 2} .0077225{col 26}{space 2} .0037829{col 37}{space 1}    2.04{col 46}{space 3}0.041{col 54}{space 4} .0003081{col 67}{space 3} .0151369
{txt}{space 8}educ {c |}{col 14}{res}{space 2} .1372953{col 26}{space 2} .0695353{col 37}{space 1}    1.97{col 46}{space 3}0.048{col 54}{space 4} .0010086{col 67}{space 3}  .273582
{txt}{space 6}income {c |}{col 14}{res}{space 2} .0404732{col 26}{space 2} .0451812{col 37}{space 1}    0.90{col 46}{space 3}0.370{col 54}{space 4}-.0480804{col 67}{space 3} .1290268
{txt}{space 12} {c |}
{space 5}marital {c |}
{space 4}Married  {c |}{col 14}{res}{space 2} .0030179{col 26}{space 2} .1317563{col 37}{space 1}    0.02{col 46}{space 3}0.982{col 54}{space 4}-.2552196{col 67}{space 3} .2612555
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
       /cut1 {c |}{col 14}{res}{space 2} .6813085{col 26}{space 2} .5272358{col 54}{space 4}-.3520546{col 67}{space 3} 1.714672
{txt}       /cut2 {c |}{col 14}{res}{space 2} 1.562214{col 26}{space 2} .5290366{col 54}{space 4} .5253215{col 67}{space 3} 2.599107
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est6{txt} stored)

{com}. 
.                         
. esttab using 1992_ALT_PIDSTR.rtf, onecell nobaselevels replace label pr2 aic bic se star(+ 0.10 * 0.05 ** 0.01) ///
>         mtitles("Original" "/(D+A)" "Weight: Disc. Freq" "Weight: Disc. Soph." "Weight: Gen Dis." "Gen Dis.") ///
>         title({c -(}\b Table XX.{c )-} "Partisan Extremity Alternative Measures of Disagreement - 1992 CNEP") ///
>         rename(disagree_total Disagreement disagree_avg Disagreement disagree_total_freq Disagreement ///
>                                 disagree_total_knowl Disagreement disagree_total_weight Disagreement genavg Disagreement) 
{res}{txt}(output written to {browse  `"1992_ALT_PIDSTR.rtf"'})

{com}. eststo clear
{txt}
{com}.                         
.                         
. /****Economic Evaluations****/
. 
. 
. eststo clear
{txt}
{com}. eststo: ologit retro i.partisan##c.disagree_total numgiven disc_knowl news_att age educ ///
>         income i.gender i.race i.employed i.marital 

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-672.22778}  
Iteration 1:{space 3}log likelihood = {res: -607.8771}  
Iteration 2:{space 3}log likelihood = {res:-606.19385}  
Iteration 3:{space 3}log likelihood = {res:-606.18774}  
Iteration 4:{space 3}log likelihood = {res:-606.18774}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       770
{txt}{col 51}LR chi2({res}16{txt}){col 67}= {res}    132.08
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-606.18774{txt}{col 51}Pseudo R2{col 67}= {res}    0.0982

{txt}{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                    retro{col 27}{c |}      Coef.{col 39}   Std. Err.{col 51}      z{col 59}   P>|z|{col 67}     [95% Con{col 80}f. Interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}partisan {c |}
{space 13}In-Partisan  {c |}{col 27}{res}{space 2} 1.319178{col 39}{space 2} .1792286{col 50}{space 1}    7.36{col 59}{space 3}0.000{col 67}{space 4} .9678967{col 80}{space 3}  1.67046
{txt}{space 11}disagree_total {c |}{col 27}{res}{space 2}   .06483{col 39}{space 2} .0638334{col 50}{space 1}    1.02{col 59}{space 3}0.310{col 67}{space 4}-.0602812{col 80}{space 3} .1899412
{txt}{space 25} {c |}
partisan#c.disagree_total {c |}
{space 13}In-Partisan  {c |}{col 27}{res}{space 2}-.1859693{col 39}{space 2} .0803373{col 50}{space 1}   -2.31{col 59}{space 3}0.021{col 67}{space 4}-.3434275{col 80}{space 3}-.0285112
{txt}{space 25} {c |}
{space 17}numgiven {c |}{col 27}{res}{space 2} .0433608{col 39}{space 2} .0598433{col 50}{space 1}    0.72{col 59}{space 3}0.469{col 67}{space 4}-.0739299{col 80}{space 3} .1606516
{txt}{space 15}disc_knowl {c |}{col 27}{res}{space 2}-.2242606{col 39}{space 2} .2094661{col 50}{space 1}   -1.07{col 59}{space 3}0.284{col 67}{space 4}-.6348066{col 80}{space 3} .1862855
{txt}{space 17}news_att {c |}{col 27}{res}{space 2}-.0994578{col 39}{space 2}  .087587{col 50}{space 1}   -1.14{col 59}{space 3}0.256{col 67}{space 4}-.2711251{col 80}{space 3} .0722096
{txt}{space 22}age {c |}{col 27}{res}{space 2}-.0109201{col 39}{space 2} .0070276{col 50}{space 1}   -1.55{col 59}{space 3}0.120{col 67}{space 4}-.0246939{col 80}{space 3} .0028536
{txt}{space 21}educ {c |}{col 27}{res}{space 2} .0281432{col 39}{space 2} .0922041{col 50}{space 1}    0.31{col 59}{space 3}0.760{col 67}{space 4}-.1525735{col 80}{space 3} .2088599
{txt}{space 19}income {c |}{col 27}{res}{space 2}-.0359057{col 39}{space 2} .0610009{col 50}{space 1}   -0.59{col 59}{space 3}0.556{col 67}{space 4}-.1554653{col 80}{space 3} .0836539
{txt}{space 25} {c |}
{space 19}gender {c |}
{space 18}Female  {c |}{col 27}{res}{space 2} -.452748{col 39}{space 2}  .166894{col 50}{space 1}   -2.71{col 59}{space 3}0.007{col 67}{space 4}-.7798541{col 80}{space 3}-.1256418
{txt}{space 25} {c |}
{space 21}race {c |}
{space 19}Black  {c |}{col 27}{res}{space 2}-.1793499{col 39}{space 2} .3720924{col 50}{space 1}   -0.48{col 59}{space 3}0.630{col 67}{space 4}-.9086375{col 80}{space 3} .5499378
{txt}{space 19}Other  {c |}{col 27}{res}{space 2} .4940554{col 39}{space 2}  .417484{col 50}{space 1}    1.18{col 59}{space 3}0.237{col 67}{space 4}-.3241982{col 80}{space 3} 1.312309
{txt}{space 25} {c |}
{space 17}employed {c |}
{space 23}2  {c |}{col 27}{res}{space 2}-.1966408{col 39}{space 2} .4634443{col 50}{space 1}   -0.42{col 59}{space 3}0.671{col 67}{space 4}-1.104975{col 80}{space 3} .7116933
{txt}{space 23}3  {c |}{col 27}{res}{space 2} .5411053{col 39}{space 2} .3088729{col 50}{space 1}    1.75{col 59}{space 3}0.080{col 67}{space 4}-.0642745{col 80}{space 3} 1.146485
{txt}{space 23}4  {c |}{col 27}{res}{space 2} .1155715{col 39}{space 2} .2434632{col 50}{space 1}    0.47{col 59}{space 3}0.635{col 67}{space 4}-.3616075{col 80}{space 3} .5927505
{txt}{space 25} {c |}
{space 18}marital {c |}
{space 17}Married  {c |}{col 27}{res}{space 2} .5072991{col 39}{space 2}  .180331{col 50}{space 1}    2.81{col 59}{space 3}0.005{col 67}{space 4} .1538569{col 80}{space 3} .8607413
{txt}{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                    /cut1 {c |}{col 27}{res}{space 2}  .518502{col 39}{space 2} .6111082{col 67}{space 4}-.6792482{col 80}{space 3} 1.716252
{txt}                    /cut2 {c |}{col 27}{res}{space 2} 2.528842{col 39}{space 2} .6195838{col 67}{space 4}  1.31448{col 80}{space 3} 3.743204
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{com}. 
. eststo: ologit retro i.partisan##c.disagree_avg numgiven disc_knowl news_att age educ ///
>         income i.gender i.race i.employed i.marital 

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-672.22778}  
Iteration 1:{space 3}log likelihood = {res:-608.49195}  
Iteration 2:{space 3}log likelihood = {res:-606.85796}  
Iteration 3:{space 3}log likelihood = {res:-606.85213}  
Iteration 4:{space 3}log likelihood = {res:-606.85213}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       770
{txt}{col 51}LR chi2({res}16{txt}){col 67}= {res}    130.75
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-606.85213{txt}{col 51}Pseudo R2{col 67}= {res}    0.0973

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                  retro{col 25}{c |}      Coef.{col 37}   Std. Err.{col 49}      z{col 57}   P>|z|{col 65}     [95% Con{col 78}f. Interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}partisan {c |}
{space 11}In-Partisan  {c |}{col 25}{res}{space 2} 1.305431{col 37}{space 2} .1809086{col 48}{space 1}    7.22{col 57}{space 3}0.000{col 65}{space 4} .9508567{col 78}{space 3} 1.660005
{txt}{space 11}disagree_avg {c |}{col 25}{res}{space 2} .2427692{col 37}{space 2} .1821432{col 48}{space 1}    1.33{col 57}{space 3}0.183{col 65}{space 4}-.1142249{col 78}{space 3} .5997633
{txt}{space 23} {c |}
partisan#c.disagree_avg {c |}
{space 11}In-Partisan  {c |}{col 25}{res}{space 2}-.5438827{col 37}{space 2} .2339728{col 48}{space 1}   -2.32{col 57}{space 3}0.020{col 65}{space 4}-1.002461{col 78}{space 3}-.0853045
{txt}{space 23} {c |}
{space 15}numgiven {c |}{col 25}{res}{space 2} .0540881{col 37}{space 2} .0598012{col 48}{space 1}    0.90{col 57}{space 3}0.366{col 65}{space 4}  -.06312{col 78}{space 3} .1712963
{txt}{space 13}disc_knowl {c |}{col 25}{res}{space 2}-.2282313{col 37}{space 2}  .209758{col 48}{space 1}   -1.09{col 57}{space 3}0.277{col 65}{space 4}-.6393494{col 78}{space 3} .1828869
{txt}{space 15}news_att {c |}{col 25}{res}{space 2}-.0998467{col 37}{space 2} .0876264{col 48}{space 1}   -1.14{col 57}{space 3}0.255{col 65}{space 4}-.2715913{col 78}{space 3} .0718979
{txt}{space 20}age {c |}{col 25}{res}{space 2}-.0112579{col 37}{space 2}  .007022{col 48}{space 1}   -1.60{col 57}{space 3}0.109{col 65}{space 4}-.0250207{col 78}{space 3} .0025049
{txt}{space 19}educ {c |}{col 25}{res}{space 2}  .013631{col 37}{space 2} .0918828{col 48}{space 1}    0.15{col 57}{space 3}0.882{col 65}{space 4}-.1664559{col 78}{space 3}  .193718
{txt}{space 17}income {c |}{col 25}{res}{space 2}-.0287205{col 37}{space 2} .0609436{col 48}{space 1}   -0.47{col 57}{space 3}0.637{col 65}{space 4}-.1481678{col 78}{space 3} .0907267
{txt}{space 23} {c |}
{space 17}gender {c |}
{space 16}Female  {c |}{col 25}{res}{space 2}-.4453033{col 37}{space 2} .1666961{col 48}{space 1}   -2.67{col 57}{space 3}0.008{col 65}{space 4}-.7720217{col 78}{space 3}-.1185849
{txt}{space 23} {c |}
{space 19}race {c |}
{space 17}Black  {c |}{col 25}{res}{space 2}-.1569048{col 37}{space 2} .3752874{col 48}{space 1}   -0.42{col 57}{space 3}0.676{col 65}{space 4}-.8924545{col 78}{space 3} .5786449
{txt}{space 17}Other  {c |}{col 25}{res}{space 2} .4990649{col 37}{space 2} .4195247{col 48}{space 1}    1.19{col 57}{space 3}0.234{col 65}{space 4}-.3231883{col 78}{space 3} 1.321318
{txt}{space 23} {c |}
{space 15}employed {c |}
{space 21}2  {c |}{col 25}{res}{space 2}-.1626815{col 37}{space 2} .4589238{col 48}{space 1}   -0.35{col 57}{space 3}0.723{col 65}{space 4}-1.062156{col 78}{space 3} .7367926
{txt}{space 21}3  {c |}{col 25}{res}{space 2} .5528894{col 37}{space 2} .3090088{col 48}{space 1}    1.79{col 57}{space 3}0.074{col 65}{space 4}-.0527566{col 78}{space 3} 1.158535
{txt}{space 21}4  {c |}{col 25}{res}{space 2} .1027824{col 37}{space 2}  .243107{col 48}{space 1}    0.42{col 57}{space 3}0.672{col 65}{space 4}-.3736985{col 78}{space 3} .5792633
{txt}{space 23} {c |}
{space 16}marital {c |}
{space 15}Married  {c |}{col 25}{res}{space 2} .5248434{col 37}{space 2} .1804335{col 48}{space 1}    2.91{col 57}{space 3}0.004{col 65}{space 4} .1712002{col 78}{space 3} .8784866
{txt}{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                  /cut1 {c |}{col 25}{res}{space 2} .5078607{col 37}{space 2} .6143686{col 65}{space 4}-.6962797{col 78}{space 3} 1.712001
{txt}                  /cut2 {c |}{col 25}{res}{space 2} 2.513865{col 37}{space 2} .6226444{col 65}{space 4} 1.293504{col 78}{space 3} 3.734226
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{com}.         
. eststo: ologit retro i.partisan##c.disagree_total_freq numgiven disc_knowl news_att age educ ///
>         income i.gender i.race i.employed i.marital 

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-591.94113}  
Iteration 1:{space 3}log likelihood = {res:-535.81519}  
Iteration 2:{space 3}log likelihood = {res:-534.48202}  
Iteration 3:{space 3}log likelihood = {res:-534.47737}  
Iteration 4:{space 3}log likelihood = {res:-534.47737}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       665
{txt}{col 51}LR chi2({res}16{txt}){col 67}= {res}    114.93
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-534.47737{txt}{col 51}Pseudo R2{col 67}= {res}    0.0971

{txt}{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                         retro{col 32}{c |}      Coef.{col 44}   Std. Err.{col 56}      z{col 64}   P>|z|{col 72}     [95% Con{col 85}f. Interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}partisan {c |}
{space 18}In-Partisan  {c |}{col 32}{res}{space 2} 1.345759{col 44}{space 2} .1895417{col 55}{space 1}    7.10{col 64}{space 3}0.000{col 72}{space 4} .9742643{col 85}{space 3} 1.717254
{txt}{space 11}disagree_total_freq {c |}{col 32}{res}{space 2} .0172663{col 44}{space 2} .0177625{col 55}{space 1}    0.97{col 64}{space 3}0.331{col 72}{space 4}-.0175477{col 85}{space 3} .0520802
{txt}{space 30} {c |}
partisan#c.disagree_total_freq {c |}
{space 18}In-Partisan  {c |}{col 32}{res}{space 2}-.0433537{col 44}{space 2} .0226981{col 55}{space 1}   -1.91{col 64}{space 3}0.056{col 72}{space 4}-.0878413{col 85}{space 3} .0011338
{txt}{space 30} {c |}
{space 22}numgiven {c |}{col 32}{res}{space 2} .0116872{col 44}{space 2} .0683483{col 55}{space 1}    0.17{col 64}{space 3}0.864{col 72}{space 4}-.1222731{col 85}{space 3} .1456475
{txt}{space 20}disc_knowl {c |}{col 32}{res}{space 2}-.0247542{col 44}{space 2} .2363123{col 55}{space 1}   -0.10{col 64}{space 3}0.917{col 72}{space 4}-.4879178{col 85}{space 3} .4384095
{txt}{space 22}news_att {c |}{col 32}{res}{space 2}-.1299512{col 44}{space 2} .0939262{col 55}{space 1}   -1.38{col 64}{space 3}0.166{col 72}{space 4}-.3140431{col 85}{space 3} .0541406
{txt}{space 27}age {c |}{col 32}{res}{space 2}-.0070048{col 44}{space 2} .0076255{col 55}{space 1}   -0.92{col 64}{space 3}0.358{col 72}{space 4}-.0219506{col 85}{space 3} .0079409
{txt}{space 26}educ {c |}{col 32}{res}{space 2}-.0379377{col 44}{space 2} .0995756{col 55}{space 1}   -0.38{col 64}{space 3}0.703{col 72}{space 4}-.2331024{col 85}{space 3} .1572269
{txt}{space 24}income {c |}{col 32}{res}{space 2}-.0644964{col 44}{space 2} .0648537{col 55}{space 1}   -0.99{col 64}{space 3}0.320{col 72}{space 4}-.1916073{col 85}{space 3} .0626145
{txt}{space 30} {c |}
{space 24}gender {c |}
{space 23}Female  {c |}{col 32}{res}{space 2} -.602507{col 44}{space 2} .1791819{col 55}{space 1}   -3.36{col 64}{space 3}0.001{col 72}{space 4}-.9536971{col 85}{space 3} -.251317
{txt}{space 30} {c |}
{space 26}race {c |}
{space 24}Black  {c |}{col 32}{res}{space 2}-.0724669{col 44}{space 2} .3834894{col 55}{space 1}   -0.19{col 64}{space 3}0.850{col 72}{space 4}-.8240923{col 85}{space 3} .6791584
{txt}{space 24}Other  {c |}{col 32}{res}{space 2} .6568141{col 44}{space 2} .4614276{col 55}{space 1}    1.42{col 64}{space 3}0.155{col 72}{space 4}-.2475675{col 85}{space 3} 1.561196
{txt}{space 30} {c |}
{space 22}employed {c |}
{space 28}2  {c |}{col 32}{res}{space 2}-.2088473{col 44}{space 2}  .499157{col 55}{space 1}   -0.42{col 64}{space 3}0.676{col 72}{space 4}-1.187177{col 85}{space 3} .7694825
{txt}{space 28}3  {c |}{col 32}{res}{space 2} .3730724{col 44}{space 2} .3505231{col 55}{space 1}    1.06{col 64}{space 3}0.287{col 72}{space 4}-.3139404{col 85}{space 3} 1.060085
{txt}{space 28}4  {c |}{col 32}{res}{space 2} .2577665{col 44}{space 2} .2554693{col 55}{space 1}    1.01{col 64}{space 3}0.313{col 72}{space 4}-.2429441{col 85}{space 3}  .758477
{txt}{space 30} {c |}
{space 23}marital {c |}
{space 22}Married  {c |}{col 32}{res}{space 2} .5269915{col 44}{space 2} .1938686{col 55}{space 1}    2.72{col 64}{space 3}0.007{col 72}{space 4} .1470159{col 85}{space 3} .9069671
{txt}{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                         /cut1 {c |}{col 32}{res}{space 2} .6108226{col 44}{space 2} .6865104{col 72}{space 4}-.7347131{col 85}{space 3} 1.956358
{txt}                         /cut2 {c |}{col 32}{res}{space 2} 2.613856{col 44}{space 2} .6954638{col 72}{space 4} 1.250772{col 85}{space 3}  3.97694
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{com}.         
. eststo: ologit retro i.partisan##c.disagree_total_knowl numgiven disc_knowl news_att age educ ///
>         income i.gender i.race i.employed i.marital 

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-591.94113}  
Iteration 1:{space 3}log likelihood = {res:-536.06858}  
Iteration 2:{space 3}log likelihood = {res:-534.75259}  
Iteration 3:{space 3}log likelihood = {res:-534.74799}  
Iteration 4:{space 3}log likelihood = {res:-534.74799}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       665
{txt}{col 51}LR chi2({res}16{txt}){col 67}= {res}    114.39
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-534.74799{txt}{col 51}Pseudo R2{col 67}= {res}    0.0966

{txt}{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                          retro{col 33}{c |}      Coef.{col 45}   Std. Err.{col 57}      z{col 65}   P>|z|{col 73}     [95% Con{col 86}f. Interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}partisan {c |}
{space 19}In-Partisan  {c |}{col 33}{res}{space 2} 1.352756{col 45}{space 2} .1894897{col 56}{space 1}    7.14{col 65}{space 3}0.000{col 73}{space 4} .9813626{col 86}{space 3} 1.724149
{txt}{space 11}disagree_total_knowl {c |}{col 33}{res}{space 2} .0213766{col 45}{space 2}  .023392{col 56}{space 1}    0.91{col 65}{space 3}0.361{col 73}{space 4}-.0244709{col 86}{space 3} .0672241
{txt}{space 31} {c |}
partisan#c.disagree_total_knowl {c |}
{space 19}In-Partisan  {c |}{col 33}{res}{space 2}-.0538873{col 45}{space 2} .0299788{col 56}{space 1}   -1.80{col 65}{space 3}0.072{col 73}{space 4}-.1126446{col 86}{space 3}   .00487
{txt}{space 31} {c |}
{space 23}numgiven {c |}{col 33}{res}{space 2} .0126392{col 45}{space 2} .0683241{col 56}{space 1}    0.18{col 65}{space 3}0.853{col 73}{space 4}-.1212736{col 86}{space 3} .1465519
{txt}{space 21}disc_knowl {c |}{col 33}{res}{space 2} -.031238{col 45}{space 2} .2372602{col 56}{space 1}   -0.13{col 65}{space 3}0.895{col 73}{space 4}-.4962595{col 86}{space 3} .4337835
{txt}{space 23}news_att {c |}{col 33}{res}{space 2}-.1281562{col 45}{space 2} .0939658{col 56}{space 1}   -1.36{col 65}{space 3}0.173{col 73}{space 4}-.3123258{col 86}{space 3} .0560133
{txt}{space 28}age {c |}{col 33}{res}{space 2}-.0070232{col 45}{space 2} .0076241{col 56}{space 1}   -0.92{col 65}{space 3}0.357{col 73}{space 4}-.0219661{col 86}{space 3} .0079197
{txt}{space 27}educ {c |}{col 33}{res}{space 2}-.0383452{col 45}{space 2} .0996696{col 56}{space 1}   -0.38{col 65}{space 3}0.700{col 73}{space 4}-.2336941{col 86}{space 3} .1570036
{txt}{space 25}income {c |}{col 33}{res}{space 2}-.0608525{col 45}{space 2} .0647783{col 56}{space 1}   -0.94{col 65}{space 3}0.348{col 73}{space 4}-.1878157{col 86}{space 3} .0661108
{txt}{space 31} {c |}
{space 25}gender {c |}
{space 24}Female  {c |}{col 33}{res}{space 2}-.5981744{col 45}{space 2} .1790029{col 56}{space 1}   -3.34{col 65}{space 3}0.001{col 73}{space 4}-.9490136{col 86}{space 3}-.2473352
{txt}{space 31} {c |}
{space 27}race {c |}
{space 25}Black  {c |}{col 33}{res}{space 2}-.0739795{col 45}{space 2} .3834343{col 56}{space 1}   -0.19{col 65}{space 3}0.847{col 73}{space 4} -.825497{col 86}{space 3}  .677538
{txt}{space 25}Other  {c |}{col 33}{res}{space 2} .6516117{col 45}{space 2} .4617706{col 56}{space 1}    1.41{col 65}{space 3}0.158{col 73}{space 4} -.253442{col 86}{space 3} 1.556665
{txt}{space 31} {c |}
{space 23}employed {c |}
{space 29}2  {c |}{col 33}{res}{space 2}-.2220944{col 45}{space 2} .4996298{col 56}{space 1}   -0.44{col 65}{space 3}0.657{col 73}{space 4}-1.201351{col 86}{space 3}  .757162
{txt}{space 29}3  {c |}{col 33}{res}{space 2} .3786779{col 45}{space 2} .3504133{col 56}{space 1}    1.08{col 65}{space 3}0.280{col 73}{space 4}-.3081196{col 86}{space 3} 1.065475
{txt}{space 29}4  {c |}{col 33}{res}{space 2} .2601911{col 45}{space 2} .2554207{col 56}{space 1}    1.02{col 65}{space 3}0.308{col 73}{space 4}-.2404243{col 86}{space 3} .7608066
{txt}{space 31} {c |}
{space 24}marital {c |}
{space 23}Married  {c |}{col 33}{res}{space 2} .5260528{col 45}{space 2} .1939421{col 56}{space 1}    2.71{col 65}{space 3}0.007{col 73}{space 4} .1459333{col 86}{space 3} .9061723
{txt}{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                          /cut1 {c |}{col 33}{res}{space 2} .6159806{col 45}{space 2} .6871826{col 73}{space 4}-.7308725{col 86}{space 3} 1.962834
{txt}                          /cut2 {c |}{col 33}{res}{space 2} 2.617317{col 45}{space 2} .6961115{col 73}{space 4} 1.252963{col 86}{space 3}  3.98167
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{com}.         
. eststo: ologit retro i.partisan##c.disagree_total_weight numgiven disc_knowl news_att age educ ///
>         income i.gender i.race i.employed i.marital 

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-668.41392}  
Iteration 1:{space 3}log likelihood = {res:-603.39684}  
Iteration 2:{space 3}log likelihood = {res:-601.61171}  
Iteration 3:{space 3}log likelihood = {res:-601.60548}  
Iteration 4:{space 3}log likelihood = {res:-601.60548}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       766
{txt}{col 51}LR chi2({res}16{txt}){col 67}= {res}    133.62
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-601.60548{txt}{col 51}Pseudo R2{col 67}= {res}    0.1000

{txt}{hline 33}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                           retro{col 34}{c |}      Coef.{col 46}   Std. Err.{col 58}      z{col 66}   P>|z|{col 74}     [95% Con{col 87}f. Interval]
{hline 33}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 24}partisan {c |}
{space 20}In-Partisan  {c |}{col 34}{res}{space 2} 1.367739{col 46}{space 2}  .174498{col 57}{space 1}    7.84{col 66}{space 3}0.000{col 74}{space 4} 1.025729{col 87}{space 3} 1.709749
{txt}{space 11}disagree_total_weight {c |}{col 34}{res}{space 2}  .036379{col 46}{space 2} .0249962{col 57}{space 1}    1.46{col 66}{space 3}0.146{col 74}{space 4}-.0126126{col 87}{space 3} .0853705
{txt}{space 32} {c |}
partisan#c.disagree_total_weight {c |}
{space 20}In-Partisan  {c |}{col 34}{res}{space 2}-.0783602{col 46}{space 2} .0310579{col 57}{space 1}   -2.52{col 66}{space 3}0.012{col 74}{space 4}-.1392326{col 87}{space 3}-.0174879
{txt}{space 32} {c |}
{space 24}numgiven {c |}{col 34}{res}{space 2} .0477067{col 46}{space 2} .0600973{col 57}{space 1}    0.79{col 66}{space 3}0.427{col 74}{space 4}-.0700818{col 87}{space 3} .1654952
{txt}{space 22}disc_knowl {c |}{col 34}{res}{space 2} -.234137{col 46}{space 2} .2113266{col 57}{space 1}   -1.11{col 66}{space 3}0.268{col 74}{space 4}-.6483296{col 87}{space 3} .1800555
{txt}{space 24}news_att {c |}{col 34}{res}{space 2}-.1023008{col 46}{space 2} .0879977{col 57}{space 1}   -1.16{col 66}{space 3}0.245{col 74}{space 4}-.2747732{col 87}{space 3} .0701715
{txt}{space 29}age {c |}{col 34}{res}{space 2}-.0112831{col 46}{space 2} .0070739{col 57}{space 1}   -1.60{col 66}{space 3}0.111{col 74}{space 4}-.0251476{col 87}{space 3} .0025815
{txt}{space 28}educ {c |}{col 34}{res}{space 2}  .033315{col 46}{space 2} .0925555{col 57}{space 1}    0.36{col 66}{space 3}0.719{col 74}{space 4}-.1480905{col 87}{space 3} .2147204
{txt}{space 26}income {c |}{col 34}{res}{space 2}-.0447956{col 46}{space 2} .0612493{col 57}{space 1}   -0.73{col 66}{space 3}0.465{col 74}{space 4}-.1648421{col 87}{space 3} .0752509
{txt}{space 32} {c |}
{space 26}gender {c |}
{space 25}Female  {c |}{col 34}{res}{space 2}-.4436375{col 46}{space 2} .1676379{col 57}{space 1}   -2.65{col 66}{space 3}0.008{col 74}{space 4}-.7722018{col 87}{space 3}-.1150731
{txt}{space 32} {c |}
{space 28}race {c |}
{space 26}Black  {c |}{col 34}{res}{space 2}-.1467648{col 46}{space 2} .3725645{col 57}{space 1}   -0.39{col 66}{space 3}0.694{col 74}{space 4}-.8769779{col 87}{space 3} .5834483
{txt}{space 26}Other  {c |}{col 34}{res}{space 2} .4970564{col 46}{space 2} .4194977{col 57}{space 1}    1.18{col 66}{space 3}0.236{col 74}{space 4}-.3251441{col 87}{space 3} 1.319257
{txt}{space 32} {c |}
{space 24}employed {c |}
{space 30}2  {c |}{col 34}{res}{space 2} -.189279{col 46}{space 2} .4650266{col 57}{space 1}   -0.41{col 66}{space 3}0.684{col 74}{space 4}-1.100715{col 87}{space 3} .7221564
{txt}{space 30}3  {c |}{col 34}{res}{space 2} .5801898{col 46}{space 2} .3106396{col 57}{space 1}    1.87{col 66}{space 3}0.062{col 74}{space 4}-.0286527{col 87}{space 3} 1.189032
{txt}{space 30}4  {c |}{col 34}{res}{space 2} .1264998{col 46}{space 2}  .244391{col 57}{space 1}    0.52{col 66}{space 3}0.605{col 74}{space 4}-.3524978{col 87}{space 3} .6054973
{txt}{space 32} {c |}
{space 25}marital {c |}
{space 24}Married  {c |}{col 34}{res}{space 2} .4924011{col 46}{space 2} .1811107{col 57}{space 1}    2.72{col 66}{space 3}0.007{col 74}{space 4} .1374307{col 87}{space 3} .8473715
{txt}{hline 33}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                           /cut1 {c |}{col 34}{res}{space 2} .5011505{col 46}{space 2} .6169083{col 74}{space 4}-.7079675{col 87}{space 3} 1.710268
{txt}                           /cut2 {c |}{col 34}{res}{space 2} 2.525457{col 46}{space 2} .6255201{col 74}{space 4}  1.29946{col 87}{space 3} 3.751454
{txt}{hline 33}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est5{txt} stored)

{com}.         
. eststo: ologit retro i.partisan##c.genavg numgiven disc_knowl news_att age educ ///
>         income i.gender i.race i.employed i.marital 

{res}{txt}Iteration 0:{space 3}log likelihood = {res:-833.95553}  
Iteration 1:{space 3}log likelihood = {res:-771.47611}  
Iteration 2:{space 3}log likelihood = {res:-770.09367}  
Iteration 3:{space 3}log likelihood = {res:-770.08878}  
Iteration 4:{space 3}log likelihood = {res:-770.08878}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       964
{txt}{col 51}LR chi2({res}16{txt}){col 67}= {res}    127.73
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log likelihood = {res}-770.08878{txt}{col 51}Pseudo R2{col 67}= {res}    0.0766

{txt}{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}            retro{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}partisan {c |}
{space 5}In-Partisan  {c |}{col 19}{res}{space 2} 2.499436{col 31}{space 2} .6735805{col 42}{space 1}    3.71{col 51}{space 3}0.000{col 59}{space 4} 1.179242{col 72}{space 3} 3.819629
{txt}{space 11}genavg {c |}{col 19}{res}{space 2} .4201464{col 31}{space 2} .1955643{col 42}{space 1}    2.15{col 51}{space 3}0.032{col 59}{space 4} .0368474{col 72}{space 3} .8034455
{txt}{space 17} {c |}
partisan#c.genavg {c |}
{space 5}In-Partisan  {c |}{col 19}{res}{space 2}-.4532788{col 31}{space 2} .2503863{col 42}{space 1}   -1.81{col 51}{space 3}0.070{col 59}{space 4} -.944027{col 72}{space 3} .0374694
{txt}{space 17} {c |}
{space 9}numgiven {c |}{col 19}{res}{space 2}-.0025674{col 31}{space 2} .0517976{col 42}{space 1}   -0.05{col 51}{space 3}0.960{col 59}{space 4}-.1040888{col 72}{space 3} .0989539
{txt}{space 7}disc_knowl {c |}{col 19}{res}{space 2}-.0850741{col 31}{space 2} .1846381{col 42}{space 1}   -0.46{col 51}{space 3}0.645{col 59}{space 4}-.4469581{col 72}{space 3} .2768098
{txt}{space 9}news_att {c |}{col 19}{res}{space 2}-.1601755{col 31}{space 2} .0777452{col 42}{space 1}   -2.06{col 51}{space 3}0.039{col 59}{space 4}-.3125532{col 72}{space 3}-.0077978
{txt}{space 14}age {c |}{col 19}{res}{space 2}-.0030976{col 31}{space 2} .0060567{col 42}{space 1}   -0.51{col 51}{space 3}0.609{col 59}{space 4}-.0149685{col 72}{space 3} .0087733
{txt}{space 13}educ {c |}{col 19}{res}{space 2} .0597189{col 31}{space 2} .0807274{col 42}{space 1}    0.74{col 51}{space 3}0.459{col 59}{space 4}-.0985039{col 72}{space 3} .2179417
{txt}{space 11}income {c |}{col 19}{res}{space 2} .0035622{col 31}{space 2} .0541043{col 42}{space 1}    0.07{col 51}{space 3}0.948{col 59}{space 4}-.1024803{col 72}{space 3} .1096048
{txt}{space 17} {c |}
{space 11}gender {c |}
{space 10}Female  {c |}{col 19}{res}{space 2}-.3956983{col 31}{space 2} .1474032{col 42}{space 1}   -2.68{col 51}{space 3}0.007{col 59}{space 4}-.6846033{col 72}{space 3}-.1067934
{txt}{space 17} {c |}
{space 13}race {c |}
{space 11}Black  {c |}{col 19}{res}{space 2}-.0949085{col 31}{space 2} .3022545{col 42}{space 1}   -0.31{col 51}{space 3}0.754{col 59}{space 4}-.6873165{col 72}{space 3} .4974995
{txt}{space 11}Other  {c |}{col 19}{res}{space 2} .4111326{col 31}{space 2} .3391822{col 42}{space 1}    1.21{col 51}{space 3}0.225{col 59}{space 4}-.2536523{col 72}{space 3} 1.075918
{txt}{space 17} {c |}
{space 9}employed {c |}
{space 15}2  {c |}{col 19}{res}{space 2} .2088706{col 31}{space 2}  .368318{col 42}{space 1}    0.57{col 51}{space 3}0.571{col 59}{space 4}-.5130195{col 72}{space 3} .9307606
{txt}{space 15}3  {c |}{col 19}{res}{space 2} .2624383{col 31}{space 2} .2729804{col 42}{space 1}    0.96{col 51}{space 3}0.336{col 59}{space 4}-.2725934{col 72}{space 3}   .79747
{txt}{space 15}4  {c |}{col 19}{res}{space 2} .1970815{col 31}{space 2} .2088717{col 42}{space 1}    0.94{col 51}{space 3}0.345{col 59}{space 4}-.2122995{col 72}{space 3} .6064625
{txt}{space 17} {c |}
{space 10}marital {c |}
{space 9}Married  {c |}{col 19}{res}{space 2} .3988247{col 31}{space 2} .1553931{col 42}{space 1}    2.57{col 51}{space 3}0.010{col 59}{space 4} .0942597{col 72}{space 3} .7033896
{txt}{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
            /cut1 {c |}{col 19}{res}{space 2} 2.280224{col 31}{space 2} .7619307{col 59}{space 4} .7868673{col 72}{space 3} 3.773581
{txt}            /cut2 {c |}{col 19}{res}{space 2} 4.200669{col 31}{space 2} .7702471{col 59}{space 4} 2.691012{col 72}{space 3} 5.710325
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est6{txt} stored)

{com}.         
. esttab using 1992_ALT_ECON.rtf, onecell nobaselevels replace label pr2 aic bic se star(+ 0.10 * 0.05 ** 0.01) ///
>         mtitles("Original" "/(D+A)" "Weight: Disc. Freq" "Weight: Disc. Soph." "Weight: Gen Dis." "Gen Dis.") ///
>         title({c -(}\b Table XX.{c )-} "Partisan Extremity Alternative Measures of Disagreement - 1992 CNEP") ///
>         rename(disagree_total Disagreement disagree_avg Disagreement disagree_total_freq Disagreement ///
>                                 disagree_total_knowl Disagreement disagree_total_weight Disagreement genavg Disagreement) 
{res}{txt}(output written to {browse  `"1992_ALT_ECON.rtf"'})

{com}. eststo clear
{txt}
{com}.                         
. /****************************************
> *****************************************
>                 2000 ANES
> *****************************************
> ****************************************/       
. 
. clear
{txt}
{com}. do "Data Cleaning - 2000 ANES.do"
{txt}
{com}. **********************************************************************
. **********************************************************************
. ***********************2000 ANES Time Series**************************
. **********************************************************************
. **********************************************************************
. **********************************************************************
. 
. 
. **********************************************************************
. ****************************Data Cleaning****************************
. **********************************************************************
. **********************************************************************
. **********************************************************************
. **********************************************************************
. 
. clear
{txt}
{com}. use "anes2000TS.dta"
{txt}
{com}. set more off
{txt}
{com}. 
. 
.                 ************************************
.                 *********Economic Assessments*******
.                 ************************************
. *Deficit*
.         *Note: both a 3pt and 5pt scale are created - the former
.         *has `3' at the end, the latter has `5'
.         label def def 1 "Larger" 2 "Same" 3 "Smaller" 
{txt}
{com}.         recode V001590 (3=1) (5=2) (1=3) (0=.) (8=.), gen(deficit_post3)
{txt}(1807 differences between V001590 and deficit_post3)

{com}.         label var deficit_post3 "Deficit Change Since 1992"
{txt}
{com}.         label values deficit_post3 def
{txt}
{com}.         
.         label def def1 1 "Much Larger" 2 "Somewhat Larger" 3 "Same" 4 "Somewhat Smaller" 5 "Much Smaller"
{txt}
{com}.         gen deficit_post5 = .
{txt}(1807 missing values generated)

{com}.         replace deficit_post5 = 1  if V001590 == 3 & V001591a == 1
{txt}(31 real changes made)

{com}.         replace deficit_post5 = 2  if V001590 == 3 & V001591a == 5
{txt}(60 real changes made)

{com}.         replace deficit_post5 = 3  if V001590 == 5
{txt}(157 real changes made)

{com}.         replace deficit_post5 = 4  if V001590 == 1 & V001591 == 5
{txt}(254 real changes made)

{com}.         replace deficit_post5 = 5  if V001590 == 1 & V001591 == 1
{txt}(230 real changes made)

{com}.         label var deficit_post5 "Deficit Change Since 1992"
{txt}
{com}.         label values deficit_post5 def1
{txt}
{com}. 
.         tab deficit_post3 

    {txt}Deficit {c |}
     Change {c |}
 Since 1992 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
     Larger {c |}{res}         93       12.62       12.62
{txt}       Same {c |}{res}        157       21.30       33.92
{txt}    Smaller {c |}{res}        487       66.08      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        737      100.00
{txt}
{com}.         tab deficit_post5 

  {txt}Deficit Change {c |}
      Since 1992 {c |}      Freq.     Percent        Cum.
{hline 17}{c +}{hline 35}
     Much Larger {c |}{res}         31        4.23        4.23
{txt} Somewhat Larger {c |}{res}         60        8.20       12.43
{txt}            Same {c |}{res}        157       21.45       33.88
{txt}Somewhat Smaller {c |}{res}        254       34.70       68.58
{txt}    Much Smaller {c |}{res}        230       31.42      100.00
{txt}{hline 17}{c +}{hline 35}
           Total {c |}{res}        732      100.00
{txt}
{com}.         
.         gen def_knowl = .
{txt}(1807 missing values generated)

{com}.         replace def_knowl = 1 if deficit_post3 == 3
{txt}(487 real changes made)

{com}.         replace def_knowl = 0 if deficit_post3 >=1 & deficit_post3 <= 2
{txt}(250 real changes made)

{com}.         label var def_knowl "Deficit Knowledge"
{txt}
{com}.         label def kno 1 "Correct" 0 "Incorrect" 
{txt}
{com}.         label values def_knowl kno
{txt}
{com}.         
.         
.                 
. *National Economy: Assessment and Responsibility*
.         *Assessment
.                 label def eco 1 "Worse" 2 "Same" 3 " Better"
{txt}
{com}.                 recode V001596 (1=3) (3=1) (5=2) (0=.) (8=.), gen(econ_post3)
{txt}(1807 differences between V001596 and econ_post3)

{com}.                 label var econ_post3 "Economy Change Since 1992"
{txt}
{com}.                 label values econ_post3 eco
{txt}
{com}.                 
.                 label def eco1 1 "Much Worse" 2 "Somewhat Worse" 3 "Same" 4 "Somewhat Better" 5 "Much Better"
{txt}
{com}.                 recode V001599 (1=5) (2=4) (3=3) (4=2) (5=1) (0=.) (8=.), gen(econ_post5)
{txt}(1635 differences between V001599 and econ_post5)

{com}.                 label var econ_post5 "Economy Change Since 1992"
{txt}
{com}.                 label values econ_post5 eco1
{txt}
{com}.         
.                 tab econ_post3

    {txt}Economy {c |}
     Change {c |}
 Since 1992 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
      Worse {c |}{res}         40        5.16        5.16
{txt}       Same {c |}{res}        172       22.19       27.35
{txt}     Better {c |}{res}        563       72.65      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        775      100.00
{txt}
{com}.                 tab econ_post5

 {txt}Economy Change {c |}
     Since 1992 {c |}      Freq.     Percent        Cum.
{hline 16}{c +}{hline 35}
     Much Worse {c |}{res}         12        1.55        1.55
{txt} Somewhat Worse {c |}{res}         28        3.62        5.17
{txt}           Same {c |}{res}        172       22.22       27.39
{txt}Somewhat Better {c |}{res}        252       32.56       59.95
{txt}    Much Better {c |}{res}        310       40.05      100.00
{txt}{hline 16}{c +}{hline 35}
          Total {c |}{res}        774      100.00
{txt}
{com}. 
.         *Responsibility*
.                 label def eco2 1 "Worse" 2 "No Effect" 3 "Better"
{txt}
{com}.                 recode  V001600 (1=3) (3=1) (5=2) (8=.) (0=.), gen(econ_respon3)
{txt}(1807 differences between V001600 and econ_respon3)

{com}.                 label var econ_respon3 "Clinton Made Economy Better/Worse?"
{txt}
{com}.                 label values econ_respon3 eco2
{txt}
{com}.                 
.                 label def eco3 1 "Much Worse" 2 "Somewhat Worse" 3 "No Effect" 4 "Somewhat Better" 5 "Much Better"
{txt}
{com}.                 recode V001603 (1=5) (2=4) (3=3) (4=2) (5=1) (0=.) (8=.), gen(econ_respon5)
{txt}(1480 differences between V001603 and econ_respon5)

{com}.                 label var econ_respon5 "Clinton Made Economy Better/Worse?"
{txt}
{com}.                 label values econ_respon5 eco3
{txt}
{com}.                 
.                 tab econ_respon3 

    {txt}Clinton {c |}
       Made {c |}
    Economy {c |}
Better/Wors {c |}
         e? {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
      Worse {c |}{res}         21        2.74        2.74
{txt}  No Effect {c |}{res}        327       42.63       45.37
{txt}     Better {c |}{res}        419       54.63      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        767      100.00
{txt}
{com}.                 tab econ_respon5

   {txt}Clinton Made {c |}
        Economy {c |}
  Better/Worse? {c |}      Freq.     Percent        Cum.
{hline 16}{c +}{hline 35}
     Much Worse {c |}{res}          6        0.78        0.78
{txt} Somewhat Worse {c |}{res}         15        1.96        2.74
{txt}      No Effect {c |}{res}        327       42.63       45.37
{txt}Somewhat Better {c |}{res}        227       29.60       74.97
{txt}    Much Better {c |}{res}        192       25.03      100.00
{txt}{hline 16}{c +}{hline 35}
          Total {c |}{res}        767      100.00
{txt}
{com}.                         
. *Clinton Admin Help Respondent Economically or Hurt? 
.         recode V001604(1=3) (3=1) (5=2) (8=.) (0=.), gen(econhelp_post)
{txt}(1807 differences between V001604 and econhelp_post)

{com}.         label var econhelp_post "Clinton Admin Help/Hurt R Econ. Personally"
{txt}
{com}.         label def eco4 1 "Hurt" 2 "Not Affected" 3 "Help"
{txt}
{com}.         label values econhelp_post eco4
{txt}
{com}.         
.                 
. *Crime Rate: Assessment and Responsibility*
.         *Assessment
.                 recode V001613 (1=3) (3=1) (5=2) (8=.) (0=.), gen(crime_post3)
{txt}(1807 differences between V001613 and crime_post3)

{com}.                 label var crime_post3 "Crime Rate Change Since 1992"
{txt}
{com}.                 label values crime_post3  eco 
{txt}
{com}.         
.                 recode V001616 (1=5) (2=4) (3=3) (4=2) (5=1) (0=.) (8=.), gen(crime_post5)
{txt}(1551 differences between V001616 and crime_post5)

{com}.                 label var crime_post5 "Crime Rate Change Since 1995"
{txt}
{com}.                 label values crime_post5 eco1
{txt}
{com}.         
.                 tab crime_post3 

 {txt}Crime Rate {c |}
     Change {c |}
 Since 1992 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
      Worse {c |}{res}        220       28.57       28.57
{txt}       Same {c |}{res}        256       33.25       61.82
{txt}     Better {c |}{res}        294       38.18      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        770      100.00
{txt}
{com}.                 tab crime_post5

     {txt}Crime Rate {c |}
   Change Since {c |}
           1995 {c |}      Freq.     Percent        Cum.
{hline 16}{c +}{hline 35}
     Much Worse {c |}{res}         82       10.66       10.66
{txt} Somewhat Worse {c |}{res}        138       17.95       28.61
{txt}           Same {c |}{res}        256       33.29       61.90
{txt}Somewhat Better {c |}{res}        214       27.83       89.73
{txt}    Much Better {c |}{res}         79       10.27      100.00
{txt}{hline 16}{c +}{hline 35}
          Total {c |}{res}        769      100.00
{txt}
{com}.         
.         *Responsibility*
.                 recode  V001617 (1=3) (3=1) (5=2) (8=.) (0=.) (9=.), gen(crime_respon3)
{txt}(1807 differences between V001617 and crime_respon3)

{com}.                 label var crime_respon3 "Clinton Made Crime Rate Better/Worse?"
{txt}
{com}.                 label values crime_respon3 eco2
{txt}
{com}.                 
.                 recode V001620 (1=5) (2=4) (3=3) (4=2) (5=1) (0=.) (8=.) (9=.), gen(crime_respon5)
{txt}(1299 differences between V001620 and crime_respon5)

{com}.                 label var crime_respon5 "Clinton Made Crime Rate Better/Worse?"
{txt}
{com}.                 label values crime_respon5 eco3
{txt}
{com}.                 
.                 tab crime_respon3 

    {txt}Clinton {c |}
 Made Crime {c |}
       Rate {c |}
Better/Wors {c |}
         e? {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
      Worse {c |}{res}         65        8.52        8.52
{txt}  No Effect {c |}{res}        508       66.58       75.10
{txt}     Better {c |}{res}        190       24.90      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        763      100.00
{txt}
{com}.                 tab crime_respon5

   {txt}Clinton Made {c |}
     Crime Rate {c |}
  Better/Worse? {c |}      Freq.     Percent        Cum.
{hline 16}{c +}{hline 35}
     Much Worse {c |}{res}         24        3.15        3.15
{txt} Somewhat Worse {c |}{res}         41        5.37        8.52
{txt}      No Effect {c |}{res}        508       66.58       75.10
{txt}Somewhat Better {c |}{res}        140       18.35       93.45
{txt}    Much Better {c |}{res}         50        6.55      100.00
{txt}{hline 16}{c +}{hline 35}
          Total {c |}{res}        763      100.00
{txt}
{com}.                 
.         
.         pwcorr deficit_post5 econ_post5 econ_respon5 crime_post5 crime_respon5, sig 

             {txt}{c |} defici~5 econ_p~5 econ_r~5 crime~t5 crime~n5
{hline 13}{c +}{hline 45}
deficit_po~5 {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
  econ_post5 {c |} {res}  0.4030   1.0000 
             {txt}{c |}{res}   0.0000
             {txt}{c |}
econ_respon5 {c |} {res}  0.2623   0.5320   1.0000 
             {txt}{c |}{res}   0.0000   0.0000
             {txt}{c |}
 crime_post5 {c |} {res}  0.2905   0.3487   0.2807   1.0000 
             {txt}{c |}{res}   0.0000   0.0000   0.0000
             {txt}{c |}
crime_resp~5 {c |} {res}  0.2241   0.3135   0.4130   0.6262   1.0000 
             {txt}{c |}{res}   0.0000   0.0000   0.0000   0.0000
             {txt}{c |}

{com}. 
.                 ************************************
.                 *********Partisanship***************
.                 ************************************
.                 
. *Party ID: Pre-Election*
.         *Full Scale
.         label def pi1 1 "Str. Dem" 2 "Weak Dem" 3 "Lean Dem" 4 "Ind." 5 "Lean Rep" 6 "Weak Rep" 7 "Str. Rep"
{txt}
{com}.         recode  V000523 (0=1) (1=2) (2=3) (3=4) (4=5) (5=6) (6=7) (7=.) (8=.) (9=.), gen(partyid)
{txt}(1807 differences between V000523 and partyid)

{com}.         label var partyid "Party ID (Pre-Election)"
{txt}
{com}.         label values partyid pi1
{txt}
{com}.         
.         *3-Point Categorical
.         gen pid_3 = . 
{txt}(1807 missing values generated)

{com}.         replace pid_3 = 1 if partyid >=1 & partyid <= 3
{txt}(889 real changes made)

{com}.         replace pid_3 = 3 if partyid == 4
{txt}(206 real changes made)

{com}.         replace pid_3 = 2 if partyid >=5 & partyid <= 7
{txt}(681 real changes made)

{com}.         label var pid_3 "Party ID (Categorical; Pre-Election)"
{txt}
{com}.         label def pi2 1 "Democrat" 3 "Independent" 2 "Republican"
{txt}
{com}.         label values pid_3 pi2
{txt}
{com}.         
.         *Republican Vs. Democrat
.         gen pid_2 = . 
{txt}(1807 missing values generated)

{com}.         replace pid_2 = 1 if partyid >=1 & partyid <= 3
{txt}(889 real changes made)

{com}.         replace pid_2 = 0 if partyid >=5 & partyid <= 7
{txt}(681 real changes made)

{com}.         label var pid_2 "PID" 
{txt}
{com}.         label def pi3 1 "Democrat" 0 "Republican"
{txt}
{com}.         label values pid_2 pi3
{txt}
{com}.         
.         tab partyid

   {txt}Party ID {c |}
(Pre-Electi {c |}
        on) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
   Str. Dem {c |}{res}        346       19.48       19.48
{txt}   Weak Dem {c |}{res}        274       15.43       34.91
{txt}   Lean Dem {c |}{res}        269       15.15       50.06
{txt}       Ind. {c |}{res}        206       11.60       61.66
{txt}   Lean Rep {c |}{res}        230       12.95       74.61
{txt}   Weak Rep {c |}{res}        215       12.11       86.71
{txt}   Str. Rep {c |}{res}        236       13.29      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,776      100.00
{txt}
{com}.         tab pid_3

   {txt}Party ID {c |}
(Categorica {c |}
         l; {c |}
Pre-Electio {c |}
         n) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
   Democrat {c |}{res}        889       50.06       50.06
{txt} Republican {c |}{res}        681       38.34       88.40
{txt}Independent {c |}{res}        206       11.60      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,776      100.00
{txt}
{com}.         tab pid_2

        {txt}PID {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
 Republican {c |}{res}        681       43.38       43.38
{txt}   Democrat {c |}{res}        889       56.62      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,570      100.00
{txt}
{com}. 
.         *Party of Incumbent President
.                 *1 = Dem; 0 = Rep
.                 gen partisan = pid_2
{txt}(237 missing values generated)

{com}.                 label var partisan "Co-Partisan to Inc. President"
{txt}
{com}.                 label def part1 1 "In-Partisan" 0 "Out-Partisan"
{txt}
{com}.                 label values partisan part1
{txt}
{com}.         
.                 
. *PID Strength (Non-Independents)*
.         gen pid_str = . 
{txt}(1807 missing values generated)

{com}.         replace pid_str = 1 if partyid == 3
{txt}(269 real changes made)

{com}.         replace pid_str = 1 if partyid == 5
{txt}(230 real changes made)

{com}.         replace pid_str = 2 if partyid == 2
{txt}(274 real changes made)

{com}.         replace pid_str = 2 if partyid == 6
{txt}(215 real changes made)

{com}.         replace pid_str = 3 if partyid == 1
{txt}(346 real changes made)

{com}.         replace pid_str = 3 if partyid == 7
{txt}(236 real changes made)

{com}.         label var pid_str "PID Str."
{txt}
{com}.         label def pi4 1 "Leaner" 2 "Weak" 3 "Strong"
{txt}
{com}.         label values pid_str pi4
{txt}
{com}. 
.         tab pid_str

   {txt}PID Str. {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
     Leaner {c |}{res}        499       31.78       31.78
{txt}       Weak {c |}{res}        489       31.15       62.93
{txt}     Strong {c |}{res}        582       37.07      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,570      100.00
{txt}
{com}.         
.         summ pid_str

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 5}pid_str {c |}{res}      1570    2.052866    .8283582          1          3
{txt}
{com}.         gen pid_str01 = (pid_str - r(min))/(r(max)-r(min))
{txt}(237 missing values generated)

{com}.         label var pid_str01 "PID Str"
{txt}
{com}.         
. 
. *PID: Post Election
.         recode  V001409 (1=1) (3=2) (5=3) (7=3) (0=.) (8=.) (9=.), gen(pid_post)
{txt}(1101 differences between V001409 and pid_post)

{com}.         label var pid_post "PID (Post-Election)"
{txt}
{com}.         label def pipost 1 "Democrat" 2 "Republican" 3 "Neither/Other"
{txt}
{com}.         label values pid_post pipost
{txt}
{com}.         
.         tab pid_post

          {txt}PID {c |}
(Post-Electio {c |}
           n) {c |}      Freq.     Percent        Cum.
{hline 14}{c +}{hline 35}
     Democrat {c |}{res}        706       45.76       45.76
{txt}   Republican {c |}{res}        522       33.83       79.59
{txt}Neither/Other {c |}{res}        315       20.41      100.00
{txt}{hline 14}{c +}{hline 35}
        Total {c |}{res}      1,543      100.00
{txt}
{com}.         
.                 
. *Pre and Post
.         tab pid_3 pid_post, row col chi2
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

   Party ID {c |}
(Categorica {c |}
         l; {c |}
Pre-Electio {c |}       PID (Post-Election)
         n) {c |}  Democrat  Republica  Neither/O {c |}     Total
{hline 12}{c +}{hline 33}{c +}{hline 10}
   Democrat {c |}{res}       636         26         88 {txt}{c |}{res}       750 
            {txt}{c |}{res}     84.80       3.47      11.73 {txt}{c |}{res}    100.00 
            {txt}{c |}{res}     90.47       5.01      29.83 {txt}{c |}{res}     49.44 
{txt}{hline 12}{c +}{hline 33}{c +}{hline 10}
 Republican {c |}{res}        37        473         95 {txt}{c |}{res}       605 
            {txt}{c |}{res}      6.12      78.18      15.70 {txt}{c |}{res}    100.00 
            {txt}{c |}{res}      5.26      91.14      32.20 {txt}{c |}{res}     39.88 
{txt}{hline 12}{c +}{hline 33}{c +}{hline 10}
Independent {c |}{res}        30         20        112 {txt}{c |}{res}       162 
            {txt}{c |}{res}     18.52      12.35      69.14 {txt}{c |}{res}    100.00 
            {txt}{c |}{res}      4.27       3.85      37.97 {txt}{c |}{res}     10.68 
{txt}{hline 12}{c +}{hline 33}{c +}{hline 10}
      Total {c |}{res}       703        519        295 {txt}{c |}{res}     1,517 
            {txt}{c |}{res}     46.34      34.21      19.45 {txt}{c |}{res}    100.00 
            {txt}{c |}{res}    100.00     100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}4{txt}) = {res} 1.3e+03  {txt} Pr = {res}0.000
{txt}
{com}.         tab partyid pid_post, row col chi2
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

  Party ID {c |}
(Pre-Elect {c |}       PID (Post-Election)
      ion) {c |}  Democrat  Republica  Neither/O {c |}     Total
{hline 11}{c +}{hline 33}{c +}{hline 10}
  Str. Dem {c |}{res}       288          2          6 {txt}{c |}{res}       296 
           {txt}{c |}{res}     97.30       0.68       2.03 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     40.97       0.39       2.03 {txt}{c |}{res}     19.51 
{txt}{hline 11}{c +}{hline 33}{c +}{hline 10}
  Weak Dem {c |}{res}       210          6         13 {txt}{c |}{res}       229 
           {txt}{c |}{res}     91.70       2.62       5.68 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     29.87       1.16       4.41 {txt}{c |}{res}     15.10 
{txt}{hline 11}{c +}{hline 33}{c +}{hline 10}
  Lean Dem {c |}{res}       138         18         69 {txt}{c |}{res}       225 
           {txt}{c |}{res}     61.33       8.00      30.67 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     19.63       3.47      23.39 {txt}{c |}{res}     14.83 
{txt}{hline 11}{c +}{hline 33}{c +}{hline 10}
      Ind. {c |}{res}        30         20        112 {txt}{c |}{res}       162 
           {txt}{c |}{res}     18.52      12.35      69.14 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}      4.27       3.85      37.97 {txt}{c |}{res}     10.68 
{txt}{hline 11}{c +}{hline 33}{c +}{hline 10}
  Lean Rep {c |}{res}        22        106         75 {txt}{c |}{res}       203 
           {txt}{c |}{res}     10.84      52.22      36.95 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}      3.13      20.42      25.42 {txt}{c |}{res}     13.38 
{txt}{hline 11}{c +}{hline 33}{c +}{hline 10}
  Weak Rep {c |}{res}        13        163         15 {txt}{c |}{res}       191 
           {txt}{c |}{res}      6.81      85.34       7.85 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}      1.85      31.41       5.08 {txt}{c |}{res}     12.59 
{txt}{hline 11}{c +}{hline 33}{c +}{hline 10}
  Str. Rep {c |}{res}         2        204          5 {txt}{c |}{res}       211 
           {txt}{c |}{res}      0.95      96.68       2.37 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}      0.28      39.31       1.69 {txt}{c |}{res}     13.91 
{txt}{hline 11}{c +}{hline 33}{c +}{hline 10}
     Total {c |}{res}       703        519        295 {txt}{c |}{res}     1,517 
           {txt}{c |}{res}     46.34      34.21      19.45 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}    100.00     100.00     100.00 {txt}{c |}{res}    100.00 

{txt}         Pearson chi2({res}12{txt}) = {res} 1.5e+03  {txt} Pr = {res}0.000
{txt}
{com}.                         
.                         
. *Bivariate Relationship with Retrospective Assessments
.         tab pid_2 deficit_post3, row col chi2
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

           {c |}    Deficit Change Since 1992
       PID {c |}    Larger       Same    Smaller {c |}     Total
{hline 11}{c +}{hline 33}{c +}{hline 10}
Republican {c |}{res}        42         66        189 {txt}{c |}{res}       297 
           {txt}{c |}{res}     14.14      22.22      63.64 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     51.85      50.00      42.95 {txt}{c |}{res}     45.48 
{txt}{hline 11}{c +}{hline 33}{c +}{hline 10}
  Democrat {c |}{res}        39         66        251 {txt}{c |}{res}       356 
           {txt}{c |}{res}     10.96      18.54      70.51 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     48.15      50.00      57.05 {txt}{c |}{res}     54.52 
{txt}{hline 11}{c +}{hline 33}{c +}{hline 10}
     Total {c |}{res}        81        132        440 {txt}{c |}{res}       653 
           {txt}{c |}{res}     12.40      20.21      67.38 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}    100.00     100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}2{txt}) = {res}  3.5456  {txt} Pr = {res}0.170
{txt}
{com}.         tab pid_2 econ_post3, row col chi2
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

           {c |}    Economy Change Since 1992
       PID {c |}     Worse       Same     Better {c |}     Total
{hline 11}{c +}{hline 33}{c +}{hline 10}
Republican {c |}{res}        15         84        208 {txt}{c |}{res}       307 
           {txt}{c |}{res}      4.89      27.36      67.75 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     45.45      60.43      40.86 {txt}{c |}{res}     45.08 
{txt}{hline 11}{c +}{hline 33}{c +}{hline 10}
  Democrat {c |}{res}        18         55        301 {txt}{c |}{res}       374 
           {txt}{c |}{res}      4.81      14.71      80.48 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     54.55      39.57      59.14 {txt}{c |}{res}     54.92 
{txt}{hline 11}{c +}{hline 33}{c +}{hline 10}
     Total {c |}{res}        33        139        509 {txt}{c |}{res}       681 
           {txt}{c |}{res}      4.85      20.41      74.74 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}    100.00     100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}2{txt}) = {res} 16.8869  {txt} Pr = {res}0.000
{txt}
{com}.         tab pid_2 econ_respon3, row col chi2
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

           {c |}       Clinton Made Economy
           {c |}          Better/Worse?
       PID {c |}     Worse  No Effect     Better {c |}     Total
{hline 11}{c +}{hline 33}{c +}{hline 10}
Republican {c |}{res}        10        198         96 {txt}{c |}{res}       304 
           {txt}{c |}{res}      3.29      65.13      31.58 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     58.82      70.71      25.46 {txt}{c |}{res}     45.10 
{txt}{hline 11}{c +}{hline 33}{c +}{hline 10}
  Democrat {c |}{res}         7         82        281 {txt}{c |}{res}       370 
           {txt}{c |}{res}      1.89      22.16      75.95 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     41.18      29.29      74.54 {txt}{c |}{res}     54.90 
{txt}{hline 11}{c +}{hline 33}{c +}{hline 10}
     Total {c |}{res}        17        280        377 {txt}{c |}{res}       674 
           {txt}{c |}{res}      2.52      41.54      55.93 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}    100.00     100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}2{txt}) = {res}134.1929  {txt} Pr = {res}0.000
{txt}
{com}.         tab pid_2 crime_post3, row col chi2
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

           {c |}   Crime Rate Change Since 1992
       PID {c |}     Worse       Same     Better {c |}     Total
{hline 11}{c +}{hline 33}{c +}{hline 10}
Republican {c |}{res}        94        117         93 {txt}{c |}{res}       304 
           {txt}{c |}{res}     30.92      38.49      30.59 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     50.54      52.94      34.83 {txt}{c |}{res}     45.10 
{txt}{hline 11}{c +}{hline 33}{c +}{hline 10}
  Democrat {c |}{res}        92        104        174 {txt}{c |}{res}       370 
           {txt}{c |}{res}     24.86      28.11      47.03 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     49.46      47.06      65.17 {txt}{c |}{res}     54.90 
{txt}{hline 11}{c +}{hline 33}{c +}{hline 10}
     Total {c |}{res}       186        221        267 {txt}{c |}{res}       674 
           {txt}{c |}{res}     27.60      32.79      39.61 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}    100.00     100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}2{txt}) = {res} 19.0793  {txt} Pr = {res}0.000
{txt}
{com}.         tab pid_2 crime_respon3, row col chi2
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

           {c |}     Clinton Made Crime Rate
           {c |}          Better/Worse?
       PID {c |}     Worse  No Effect     Better {c |}     Total
{hline 11}{c +}{hline 33}{c +}{hline 10}
Republican {c |}{res}        39        227         35 {txt}{c |}{res}       301 
           {txt}{c |}{res}     12.96      75.42      11.63 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     68.42      51.36      20.59 {txt}{c |}{res}     44.99 
{txt}{hline 11}{c +}{hline 33}{c +}{hline 10}
  Democrat {c |}{res}        18        215        135 {txt}{c |}{res}       368 
           {txt}{c |}{res}      4.89      58.42      36.68 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     31.58      48.64      79.41 {txt}{c |}{res}     55.01 
{txt}{hline 11}{c +}{hline 33}{c +}{hline 10}
     Total {c |}{res}        57        442        170 {txt}{c |}{res}       669 
           {txt}{c |}{res}      8.52      66.07      25.41 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}    100.00     100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}2{txt}) = {res} 60.7858  {txt} Pr = {res}0.000
{txt}
{com}. 
.         tab pid_2 deficit_post5, row col chi2
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

           {c |}               Deficit Change Since 1992
       PID {c |} Much Larg  Somewhat        Same  Somewhat   Much Smal {c |}     Total
{hline 11}{c +}{hline 55}{c +}{hline 10}
Republican {c |}{res}        11         29         66        112         75 {txt}{c |}{res}       293 
           {txt}{c |}{res}      3.75       9.90      22.53      38.23      25.60 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     40.74      55.77      50.00      49.34      35.71 {txt}{c |}{res}     45.22 
{txt}{hline 11}{c +}{hline 55}{c +}{hline 10}
  Democrat {c |}{res}        16         23         66        115        135 {txt}{c |}{res}       355 
           {txt}{c |}{res}      4.51       6.48      18.59      32.39      38.03 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     59.26      44.23      50.00      50.66      64.29 {txt}{c |}{res}     54.78 
{txt}{hline 11}{c +}{hline 55}{c +}{hline 10}
     Total {c |}{res}        27         52        132        227        210 {txt}{c |}{res}       648 
           {txt}{c |}{res}      4.17       8.02      20.37      35.03      32.41 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}    100.00     100.00     100.00     100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}4{txt}) = {res} 12.9875  {txt} Pr = {res}0.011
{txt}
{com}.         tab pid_2 econ_post5, row col chi2
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

           {c |}               Economy Change Since 1992
       PID {c |} Much Wors  Somewhat        Same  Somewhat   Much Bett {c |}     Total
{hline 11}{c +}{hline 55}{c +}{hline 10}
Republican {c |}{res}         2         13         84        123         84 {txt}{c |}{res}       306 
           {txt}{c |}{res}      0.65       4.25      27.45      40.20      27.45 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     20.00      56.52      60.43      54.19      29.89 {txt}{c |}{res}     45.00 
{txt}{hline 11}{c +}{hline 55}{c +}{hline 10}
  Democrat {c |}{res}         8         10         55        104        197 {txt}{c |}{res}       374 
           {txt}{c |}{res}      2.14       2.67      14.71      27.81      52.67 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     80.00      43.48      39.57      45.81      70.11 {txt}{c |}{res}     55.00 
{txt}{hline 11}{c +}{hline 55}{c +}{hline 10}
     Total {c |}{res}        10         23        139        227        281 {txt}{c |}{res}       680 
           {txt}{c |}{res}      1.47       3.38      20.44      33.38      41.32 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}    100.00     100.00     100.00     100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}4{txt}) = {res} 50.7811  {txt} Pr = {res}0.000
{txt}
{com}.         tab pid_2 econ_respon5, row col chi2
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

           {c |}           Clinton Made Economy Better/Worse?
       PID {c |} Much Wors  Somewhat   No Effect  Somewhat   Much Bett {c |}     Total
{hline 11}{c +}{hline 55}{c +}{hline 10}
Republican {c |}{res}         3          7        198         70         26 {txt}{c |}{res}       304 
           {txt}{c |}{res}      0.99       2.30      65.13      23.03       8.55 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     60.00      58.33      70.71      34.48      14.94 {txt}{c |}{res}     45.10 
{txt}{hline 11}{c +}{hline 55}{c +}{hline 10}
  Democrat {c |}{res}         2          5         82        133        148 {txt}{c |}{res}       370 
           {txt}{c |}{res}      0.54       1.35      22.16      35.95      40.00 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     40.00      41.67      29.29      65.52      85.06 {txt}{c |}{res}     54.90 
{txt}{hline 11}{c +}{hline 55}{c +}{hline 10}
     Total {c |}{res}         5         12        280        203        174 {txt}{c |}{res}       674 
           {txt}{c |}{res}      0.74       1.78      41.54      30.12      25.82 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}    100.00     100.00     100.00     100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}4{txt}) = {res}148.6449  {txt} Pr = {res}0.000
{txt}
{com}.         tab pid_2 crime_post5, row col chi2
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

           {c |}              Crime Rate Change Since 1995
       PID {c |} Much Wors  Somewhat        Same  Somewhat   Much Bett {c |}     Total
{hline 11}{c +}{hline 55}{c +}{hline 10}
Republican {c |}{res}        33         61        117         73         20 {txt}{c |}{res}       304 
           {txt}{c |}{res}     10.86      20.07      38.49      24.01       6.58 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     48.53      51.69      52.94      37.82      27.40 {txt}{c |}{res}     45.17 
{txt}{hline 11}{c +}{hline 55}{c +}{hline 10}
  Democrat {c |}{res}        35         57        104        120         53 {txt}{c |}{res}       369 
           {txt}{c |}{res}      9.49      15.45      28.18      32.52      14.36 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     51.47      48.31      47.06      62.18      72.60 {txt}{c |}{res}     54.83 
{txt}{hline 11}{c +}{hline 55}{c +}{hline 10}
     Total {c |}{res}        68        118        221        193         73 {txt}{c |}{res}       673 
           {txt}{c |}{res}     10.10      17.53      32.84      28.68      10.85 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}    100.00     100.00     100.00     100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}4{txt}) = {res} 21.2428  {txt} Pr = {res}0.000
{txt}
{com}.         tab pid_2 crime_respon5, row col chi2
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

           {c |}         Clinton Made Crime Rate Better/Worse?
       PID {c |} Much Wors  Somewhat   No Effect  Somewhat   Much Bett {c |}     Total
{hline 11}{c +}{hline 55}{c +}{hline 10}
Republican {c |}{res}        13         26        227         27          8 {txt}{c |}{res}       301 
           {txt}{c |}{res}      4.32       8.64      75.42       8.97       2.66 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     68.42      68.42      51.36      21.77      17.39 {txt}{c |}{res}     44.99 
{txt}{hline 11}{c +}{hline 55}{c +}{hline 10}
  Democrat {c |}{res}         6         12        215         97         38 {txt}{c |}{res}       368 
           {txt}{c |}{res}      1.63       3.26      58.42      26.36      10.33 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     31.58      31.58      48.64      78.23      82.61 {txt}{c |}{res}     55.01 
{txt}{hline 11}{c +}{hline 55}{c +}{hline 10}
     Total {c |}{res}        19         38        442        124         46 {txt}{c |}{res}       669 
           {txt}{c |}{res}      2.84       5.68      66.07      18.54       6.88 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}    100.00     100.00     100.00     100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}4{txt}) = {res} 61.0463  {txt} Pr = {res}0.000
{txt}
{com}. 
.         
.                 
.                 *****************************************************
.                 *********Network Size and Disagreement***************
.                 *****************************************************
. 
. *Network Size (# Listed Discussants)
.         gen names = . 
{txt}(1807 missing values generated)

{com}.         replace names = 4 if V001702 == 1 & V001701 == 1 & V001700 == 1 & V001699 == 1
{txt}(327 real changes made)

{com}.         replace names = 3 if V001702 == 5 & V001701 == 1 & V001700 == 1 & V001699 == 1
{txt}(223 real changes made)

{com}.         replace names = 2 if V001701 == 5 & V001700 == 1 & V001699 == 1
{txt}(311 real changes made)

{com}.         replace names = 1 if V001700 == 5 & V001699 == 1
{txt}(290 real changes made)

{com}.         replace names = 0 if V001699 == 5
{txt}(399 real changes made)

{com}.         label var names "# Listed Disc."
{txt}
{com}. 
.         gen names1 = . 
{txt}(1807 missing values generated)

{com}.         replace names1 = names
{txt}(1550 real changes made)

{com}.         replace names1 = . if names1 == 0
{txt}(399 real changes made, 399 to missing)

{com}.         label var names1 "Network Size"
{txt}
{com}. 
.         tab names 

   {txt}# Listed {c |}
      Disc. {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        399       25.74       25.74
{txt}          1 {c |}{res}        290       18.71       44.45
{txt}          2 {c |}{res}        311       20.06       64.52
{txt}          3 {c |}{res}        223       14.39       78.90
{txt}          4 {c |}{res}        327       21.10      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,550      100.00
{txt}
{com}.         tab names1

    {txt}Network {c |}
       Size {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}        290       25.20       25.20
{txt}          2 {c |}{res}        311       27.02       52.22
{txt}          3 {c |}{res}        223       19.37       71.59
{txt}          4 {c |}{res}        327       28.41      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,151      100.00
{txt}
{com}.         
.         summ names1 

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 6}names1 {c |}{res}      1151    2.509991    1.150287          1          4
{txt}
{com}.         gen numgiven01 = (names1 - r(min))/(r(max)-r(min))
{txt}(656 missing values generated)

{com}.         label var numgiven01 "Network Size"
{txt}
{com}.         
.         
.         
. 
. *Candidate Disagreement
.         *Discussant Vote Choice
.                 foreach var in V001710 V001718 V001726 V001734 {c -(}
{txt}  2{com}.                         tab `var'
{txt}  3{com}.                 {c )-}

{txt}Z12. How name 1 voted in election {c |}      Freq.     Percent        Cum.
{hline 34}{c +}{hline 35}
   00. NA. INAP, 5, 8, 9, 0 in Z1 {c |}{res}        655       36.25       36.25
{txt}                       1. AL GORE {c |}{res}        442       24.46       60.71
{txt}                 3. GEORGE W BUSH {c |}{res}        479       26.51       87.22
{txt}5. SOME OTHER CANDIDATE (SPECIFY) {c |}{res}         46        2.55       89.76
{txt}                   7. DIDN'T VOTE {c |}{res}        103        5.70       95.46
{txt}      8. INELIGIBLE TO VOTE [VOL] {c |}{res}         16        0.89       96.35
{txt}             98. DK - DON'T PROBE {c |}{res}         62        3.43       99.78
{txt}             99. RF - DON'T PROBE {c |}{res}          4        0.22      100.00
{txt}{hline 34}{c +}{hline 35}
                            Total {c |}{res}      1,807      100.00

{txt}Z16. How name 2 voted in election {c |}      Freq.     Percent        Cum.
{hline 34}{c +}{hline 35}
   00. NA. INAP, 5, 8, 9, 0 in Z3 {c |}{res}        946       52.35       52.35
{txt}                       1. AL GORE {c |}{res}        336       18.59       70.95
{txt}                 3. GEORGE W BUSH {c |}{res}        342       18.93       89.87
{txt}5. SOME OTHER CANDIDATE [SPECIFY] {c |}{res}         30        1.66       91.53
{txt}                   7. DIDN'T VOTE {c |}{res}         75        4.15       95.68
{txt}      8. INELIGIBLE TO VOTE [VOL] {c |}{res}         10        0.55       96.24
{txt}             98. DK - DON'T PROBE {c |}{res}         66        3.65       99.89
{txt}             99. RF - DON'T PROBE {c |}{res}          2        0.11      100.00
{txt}{hline 34}{c +}{hline 35}
                            Total {c |}{res}      1,807      100.00

      {txt}Z20. How name 3 voted in election {c |}      Freq.     Percent        Cum.
{hline 40}{c +}{hline 35}
         00. NA. INAP, 5, 8, 9, 0 in Z1 {c |}{res}      1,257       69.56       69.56
{txt}                             1. AL GORE {c |}{res}        201       11.12       80.69
{txt}                       3. GEORGE W BUSH {c |}{res}        230       12.73       93.41
{txt}5. SOME OTHER CANDIDATE (SPECIFY) [SPEC {c |}{res}         12        0.66       94.08
{txt}                         7. DIDN'T VOTE {c |}{res}         43        2.38       96.46
{txt}            8. INELIGIBLE TO VOTE [VOL] {c |}{res}          9        0.50       96.96
{txt}                   98. DK - DON'T PROBE {c |}{res}         55        3.04      100.00
{txt}{hline 40}{c +}{hline 35}
                                  Total {c |}{res}      1,807      100.00

{txt}Z24. How name 4 voted in election {c |}      Freq.     Percent        Cum.
{hline 34}{c +}{hline 35}
   00. NA. INAP, 5, 8, 9, 0 in Z1 {c |}{res}      1,480       81.90       81.90
{txt}                       1. AL GORE {c |}{res}        105        5.81       87.71
{txt}                 3. GEORGE W BUSH {c |}{res}        147        8.14       95.85
{txt}5. SOME OTHER CANDIDATE (SPECIFY) {c |}{res}          6        0.33       96.18
{txt}                   7. DIDN'T VOTE {c |}{res}         26        1.44       97.62
{txt}      8. INELIGIBLE TO VOTE [VOL] {c |}{res}          6        0.33       97.95
{txt}             98. DK - DON'T PROBE {c |}{res}         36        1.99       99.94
{txt}             99. RF - DON'T PROBE {c |}{res}          1        0.06      100.00
{txt}{hline 34}{c +}{hline 35}
                            Total {c |}{res}      1,807      100.00
{txt}
{com}.                 *1 = Al Gore
.                 *3 = George Bush
.                 *5 = Some Other Candidate
.                 *7 = Didn't vote
.                 *8 = ineliglb to vote
.                 *98 = dk
.                 *99 = ref
. 
.         *Respondent vote choice*
.                         tab V001249

          {txt}C6. R vote cast for President {c |}      Freq.     Percent        Cum.
{hline 40}{c +}{hline 35}
          0. NA. INAP, 5, 8, 9, 0 in C5 {c |}{res}        629       34.81       34.81
{txt}                            1. AL GORE  {c |}{res}        590       32.65       67.46
{txt}2. HOWARD PHILLIPS-CONSTITUTION PARTY C {c |}{res}          1        0.06       67.52
{txt}                      3. GEORGE W. BUSH {c |}{res}        530       29.33       96.85
{txt}   4. HARRY BROWN-LIBERTARIAN CANDIDATE {c |}{res}          4        0.22       97.07
{txt}                       5. PAT BUCHANAN  {c |}{res}          3        0.17       97.23
{txt}                         6. RALPH NADER {c |}{res}         33        1.83       99.06
{txt}           7. R REPORTS VOTING FOR SELF {c |}{res}          1        0.06       99.11
{txt}                                 8. DK  {c |}{res}          3        0.17       99.28
{txt}                                 9. RF  {c |}{res}         13        0.72      100.00
{txt}{hline 40}{c +}{hline 35}
                                  Total {c |}{res}      1,807      100.00
{txt}
{com}.                         *1 = Gore
.                         *2 = howard philips (n = 1)
.                         *3 = Bush
.                         *4 = Libertarian (n=4)
.                         *5 = Pat Buchanan (n =3)
.                         *6 = Nader (n=33)
.                         *7 = reports voting for self
.                         *0 = NA/INAP
.         
.         
. *Disagreement
.         *need to create indices for 'agreement' and 'disagreement'
.         *agreement: gore/gore, bush/bush; 
.         *disagree: gore/bush, bush/gore, nader/gore, nader/bush, gore/other, bush/other, remainder 3rd party/bush or gore
.                 
.         *Agree = 1, Disagree = 0
.                 label def agr 1 "Agree" 0 "Disagree" 
{txt}
{com}.                 gen d1_votea = . 
{txt}(1807 missing values generated)

{com}.                 replace d1_votea = 1 if V001249 == 1 & V001710 == 1
{txt}(301 real changes made)

{com}.                 replace d1_votea = 1 if V001249 == 3 & V001710 == 3
{txt}(307 real changes made)

{com}.                 replace d1_votea = 0  if V001249 == 1 & V001710 == 3
{txt}(92 real changes made)

{com}.                 replace d1_votea = 0  if V001249 == 3 & V001710 == 1
{txt}(77 real changes made)

{com}.                 replace d1_votea = 0  if V001249 == 1 & V001710 == 5
{txt}(11 real changes made)

{com}.                 replace d1_votea = 0  if V001249 == 3 & V001710 == 5
{txt}(9 real changes made)

{com}.                 replace d1_votea = 0  if V001249 == 2 & V001710 == 1
{txt}(0 real changes made)

{com}.                 replace d1_votea = 0  if V001249 == 2 & V001710 == 3
{txt}(1 real change made)

{com}.                 replace d1_votea = 0  if V001249 == 4 & V001710 == 1
{txt}(2 real changes made)

{com}.                 replace d1_votea = 0  if V001249 == 4 & V001710 == 3
{txt}(1 real change made)

{com}.                 replace d1_votea = 0  if V001249 == 5 & V001710 == 1
{txt}(0 real changes made)

{com}.                 replace d1_votea = 0  if V001249 == 5 & V001710 == 3
{txt}(0 real changes made)

{com}.                 replace d1_votea = 0  if V001249 == 6 & V001710 == 1
{txt}(10 real changes made)

{com}.                 replace d1_votea = 0  if V001249 == 6 & V001710 == 3
{txt}(11 real changes made)

{com}.                 label var d1_votea "D1 Vote Agreement"
{txt}
{com}.                 label values d1_votea agr
{txt}
{com}.                 
.                 
.                 gen d2_votea = . 
{txt}(1807 missing values generated)

{com}.                 replace d2_votea = 1 if V001249 == 1 & V001718 == 1
{txt}(226 real changes made)

{com}.                 replace d2_votea = 1 if V001249 == 3 & V001718 == 3
{txt}(221 real changes made)

{com}.                 replace d2_votea = 0  if V001249 == 1 & V001718 == 3
{txt}(73 real changes made)

{com}.                 replace d2_votea = 0  if V001249 == 3 & V001718 == 1
{txt}(62 real changes made)

{com}.                 replace d2_votea = 0  if V001249 == 1 & V001718 == 5
{txt}(10 real changes made)

{com}.                 replace d2_votea = 0  if V001249 == 3 & V001718 == 5
{txt}(8 real changes made)

{com}.                 replace d2_votea = 0  if V001249 == 2 & V001718 == 1
{txt}(0 real changes made)

{com}.                 replace d2_votea = 0  if V001249 == 2 & V001718 == 3
{txt}(0 real changes made)

{com}.                 replace d2_votea = 0  if V001249 == 4 & V001718 == 1
{txt}(2 real changes made)

{com}.                 replace d2_votea = 0  if V001249 == 4 & V001718 == 3
{txt}(0 real changes made)

{com}.                 replace d2_votea = 0  if V001249 == 5 & V001718 == 1
{txt}(1 real change made)

{com}.                 replace d2_votea = 0  if V001249 == 5 & V001718 == 3
{txt}(0 real changes made)

{com}.                 replace d2_votea = 0  if V001249 == 6 & V001718 == 1
{txt}(10 real changes made)

{com}.                 replace d2_votea = 0  if V001249 == 6 & V001718 == 3
{txt}(5 real changes made)

{com}.                 label var d2_votea "D2 Vote Agreement"
{txt}
{com}.                 label values d2_votea agr
{txt}
{com}.                 
.                                 
.                 gen d3_votea = . 
{txt}(1807 missing values generated)

{com}.                 replace d3_votea = 1 if V001249 == 1 & V001726 == 1
{txt}(137 real changes made)

{com}.                 replace d3_votea = 1 if V001249 == 3 & V001726 == 3
{txt}(149 real changes made)

{com}.                 replace d3_votea = 0  if V001249 == 1 & V001726 == 3
{txt}(53 real changes made)

{com}.                 replace d3_votea = 0  if V001249 == 3 & V001726 == 1
{txt}(43 real changes made)

{com}.                 replace d3_votea = 0  if V001249 == 1 & V001726 == 5
{txt}(3 real changes made)

{com}.                 replace d3_votea = 0  if V001249 == 3 & V001726 == 5
{txt}(4 real changes made)

{com}.                 replace d3_votea = 0  if V001249 == 2 & V001726 == 1
{txt}(0 real changes made)

{com}.                 replace d3_votea = 0  if V001249 == 2 & V001726 == 3
{txt}(1 real change made)

{com}.                 replace d3_votea = 0  if V001249 == 4 & V001726 == 1
{txt}(1 real change made)

{com}.                 replace d3_votea = 0  if V001249 == 4 & V001726 == 3
{txt}(1 real change made)

{com}.                 replace d3_votea = 0  if V001249 == 5 & V001726 == 1
{txt}(0 real changes made)

{com}.                 replace d3_votea = 0  if V001249 == 5 & V001726 == 3
{txt}(0 real changes made)

{com}.                 replace d3_votea = 0  if V001249 == 6 & V001726 == 1
{txt}(4 real changes made)

{com}.                 replace d3_votea = 0  if V001249 == 6 & V001726 == 3
{txt}(4 real changes made)

{com}.                 label var d3_votea "D4 Vote Agreement"
{txt}
{com}.                 label values d3_votea agr
{txt}
{com}. 
. 
.                 gen d4_votea = . 
{txt}(1807 missing values generated)

{com}.                 replace d4_votea = 1 if V001249 == 1 & V001734 == 1
{txt}(70 real changes made)

{com}.                 replace d4_votea = 1 if V001249 == 3 & V001734 == 3
{txt}(93 real changes made)

{com}.                 replace d4_votea = 0  if V001249 == 1 & V001734 == 3
{txt}(41 real changes made)

{com}.                 replace d4_votea = 0  if V001249 == 3 & V001734 == 1
{txt}(24 real changes made)

{com}.                 replace d4_votea = 0  if V001249 == 1 & V001734 == 5
{txt}(1 real change made)

{com}.                 replace d4_votea = 0  if V001249 == 3 & V001734 == 5
{txt}(2 real changes made)

{com}.                 replace d4_votea = 0  if V001249 == 2 & V001734 == 1
{txt}(0 real changes made)

{com}.                 replace d4_votea = 0  if V001249 == 2 & V001734 == 3
{txt}(0 real changes made)

{com}.                 replace d4_votea = 0  if V001249 == 4 & V001734 == 1
{txt}(0 real changes made)

{com}.                 replace d4_votea = 0  if V001249 == 4 & V001734 == 3
{txt}(0 real changes made)

{com}.                 replace d4_votea = 0  if V001249 == 5 & V001734 == 1
{txt}(0 real changes made)

{com}.                 replace d4_votea = 0  if V001249 == 5 & V001734 == 3
{txt}(0 real changes made)

{com}.                 replace d4_votea = 0  if V001249 == 6 & V001734 == 1
{txt}(2 real changes made)

{com}.                 replace d4_votea = 0  if V001249 == 6 & V001734 == 3
{txt}(0 real changes made)

{com}.                 label var d4_votea "D4 Vote Agreement"
{txt}
{com}.                 label values d4_votea agr
{txt}
{com}.         
.                 tab d1_votea 

    {txt}D1 Vote {c |}
  Agreement {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
   Disagree {c |}{res}        214       26.03       26.03
{txt}      Agree {c |}{res}        608       73.97      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        822      100.00
{txt}
{com}.                 tab d2_votea 

    {txt}D2 Vote {c |}
  Agreement {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
   Disagree {c |}{res}        171       27.67       27.67
{txt}      Agree {c |}{res}        447       72.33      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        618      100.00
{txt}
{com}.                 tab d3_votea

    {txt}D4 Vote {c |}
  Agreement {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
   Disagree {c |}{res}        114       28.50       28.50
{txt}      Agree {c |}{res}        286       71.50      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        400      100.00
{txt}
{com}.                 tab d4_votea

    {txt}D4 Vote {c |}
  Agreement {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
   Disagree {c |}{res}         70       30.04       30.04
{txt}      Agree {c |}{res}        163       69.96      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        233      100.00
{txt}
{com}.         
.         *Disagreement (=1; agree =0)
.                 label def disagr 1 "Disagree" 0 "Agree"
{txt}
{com}.                                         
.                 gen d1_voted = . 
{txt}(1807 missing values generated)

{com}.                 replace d1_voted = 0 if V001249 == 1 & V001710 == 1
{txt}(301 real changes made)

{com}.                 replace d1_voted = 0 if V001249 == 3 & V001710 == 3
{txt}(307 real changes made)

{com}.                 replace d1_voted = 1  if V001249 == 1 & V001710 == 3
{txt}(92 real changes made)

{com}.                 replace d1_voted = 1  if V001249 == 3 & V001710 == 1
{txt}(77 real changes made)

{com}.                 replace d1_voted = 1  if V001249 == 1 & V001710 == 5
{txt}(11 real changes made)

{com}.                 replace d1_voted = 1  if V001249 == 3 & V001710 == 5
{txt}(9 real changes made)

{com}.                 replace d1_voted = 1  if V001249 == 2 & V001710 == 1
{txt}(0 real changes made)

{com}.                 replace d1_voted = 1  if V001249 == 2 & V001710 == 3
{txt}(1 real change made)

{com}.                 replace d1_voted = 1  if V001249 == 4 & V001710 == 1
{txt}(2 real changes made)

{com}.                 replace d1_voted = 1  if V001249 == 4 & V001710 == 3
{txt}(1 real change made)

{com}.                 replace d1_voted = 1  if V001249 == 5 & V001710 == 1
{txt}(0 real changes made)

{com}.                 replace d1_voted = 1  if V001249 == 5 & V001710 == 3
{txt}(0 real changes made)

{com}.                 replace d1_voted = 1  if V001249 == 6 & V001710 == 1
{txt}(10 real changes made)

{com}.                 replace d1_voted = 1  if V001249 == 6 & V001710 == 3
{txt}(11 real changes made)

{com}.                 label var d1_voted "D1 Vote Disagreement"
{txt}
{com}.                 label values d1_voted disagr
{txt}
{com}.                 
.                 gen d2_voted = . 
{txt}(1807 missing values generated)

{com}.                 replace d2_voted = 0 if V001249 == 1 & V001718 == 1
{txt}(226 real changes made)

{com}.                 replace d2_voted = 0 if V001249 == 3 & V001718 == 3
{txt}(221 real changes made)

{com}.                 replace d2_voted = 1  if V001249 == 1 & V001718 == 3
{txt}(73 real changes made)

{com}.                 replace d2_voted = 1  if V001249 == 3 & V001718 == 1
{txt}(62 real changes made)

{com}.                 replace d2_voted = 1  if V001249 == 1 & V001718 == 5
{txt}(10 real changes made)

{com}.                 replace d2_voted = 1  if V001249 == 3 & V001718 == 5
{txt}(8 real changes made)

{com}.                 replace d2_voted = 1  if V001249 == 2 & V001718 == 1
{txt}(0 real changes made)

{com}.                 replace d2_voted = 1  if V001249 == 2 & V001718 == 3
{txt}(0 real changes made)

{com}.                 replace d2_voted = 1  if V001249 == 4 & V001718 == 1
{txt}(2 real changes made)

{com}.                 replace d2_voted = 1  if V001249 == 4 & V001718 == 3
{txt}(0 real changes made)

{com}.                 replace d2_voted = 1  if V001249 == 5 & V001718 == 1
{txt}(1 real change made)

{com}.                 replace d2_voted = 1  if V001249 == 5 & V001718 == 3
{txt}(0 real changes made)

{com}.                 replace d2_voted = 1  if V001249 == 6 & V001718 == 1
{txt}(10 real changes made)

{com}.                 replace d2_voted = 1  if V001249 == 6 & V001718 == 3
{txt}(5 real changes made)

{com}.                 label var d2_voted "D2 Vote Disagreement"
{txt}
{com}.                 label values d2_voted disagr
{txt}
{com}.                 
.                 gen d3_voted = . 
{txt}(1807 missing values generated)

{com}.                 replace d3_voted = 0 if V001249 == 1 & V001726 == 1
{txt}(137 real changes made)

{com}.                 replace d3_voted = 0 if V001249 == 3 & V001726 == 3
{txt}(149 real changes made)

{com}.                 replace d3_voted = 1  if V001249 == 1 & V001726 == 3
{txt}(53 real changes made)

{com}.                 replace d3_voted = 1  if V001249 == 3 & V001726 == 1
{txt}(43 real changes made)

{com}.                 replace d3_voted = 1  if V001249 == 1 & V001726 == 5
{txt}(3 real changes made)

{com}.                 replace d3_voted = 1  if V001249 == 3 & V001726 == 5
{txt}(4 real changes made)

{com}.                 replace d3_voted = 1  if V001249 == 2 & V001726 == 1
{txt}(0 real changes made)

{com}.                 replace d3_voted = 1  if V001249 == 2 & V001726 == 3
{txt}(1 real change made)

{com}.                 replace d3_voted = 1  if V001249 == 4 & V001726 == 1
{txt}(1 real change made)

{com}.                 replace d3_voted = 1  if V001249 == 4 & V001726 == 3
{txt}(1 real change made)

{com}.                 replace d3_voted = 1  if V001249 == 5 & V001726 == 1
{txt}(0 real changes made)

{com}.                 replace d3_voted = 1  if V001249 == 5 & V001726 == 3
{txt}(0 real changes made)

{com}.                 replace d3_voted = 1  if V001249 == 6 & V001726 == 1
{txt}(4 real changes made)

{com}.                 replace d3_voted = 1  if V001249 == 6 & V001726 == 3
{txt}(4 real changes made)

{com}.                 label var d3_voted "D3 Vote Disagreement"
{txt}
{com}.                 label values d3_voted disagr
{txt}
{com}.                 
.                 gen d4_voted = . 
{txt}(1807 missing values generated)

{com}.                 replace d4_voted = 0 if V001249 == 1 & V001734 == 1
{txt}(70 real changes made)

{com}.                 replace d4_voted = 0 if V001249 == 3 & V001734 == 3
{txt}(93 real changes made)

{com}.                 replace d4_voted = 1  if V001249 == 1 & V001734 == 3
{txt}(41 real changes made)

{com}.                 replace d4_voted = 1  if V001249 == 3 & V001734 == 1
{txt}(24 real changes made)

{com}.                 replace d4_voted = 1  if V001249 == 1 & V001734 == 5
{txt}(1 real change made)

{com}.                 replace d4_voted = 1  if V001249 == 3 & V001734 == 5
{txt}(2 real changes made)

{com}.                 replace d4_voted = 1  if V001249 == 2 & V001734 == 1
{txt}(0 real changes made)

{com}.                 replace d4_voted = 1  if V001249 == 2 & V001734 == 3
{txt}(0 real changes made)

{com}.                 replace d4_voted = 1  if V001249 == 4 & V001734 == 1
{txt}(0 real changes made)

{com}.                 replace d4_voted = 1  if V001249 == 4 & V001734 == 3
{txt}(0 real changes made)

{com}.                 replace d4_voted = 1  if V001249 == 5 & V001734 == 1
{txt}(0 real changes made)

{com}.                 replace d4_voted = 1  if V001249 == 5 & V001734 == 3
{txt}(0 real changes made)

{com}.                 replace d4_voted = 1  if V001249 == 6 & V001734 == 1
{txt}(2 real changes made)

{com}.                 replace d4_voted = 1  if V001249 == 6 & V001734 == 3
{txt}(0 real changes made)

{com}.                 label var d4_voted "D4 Vote Disagreement"
{txt}
{com}.                 label values d4_voted disagr
{txt}
{com}.                 
.                 tab d1_voted

    {txt}D1 Vote {c |}
Disagreemen {c |}
          t {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
      Agree {c |}{res}        608       73.97       73.97
{txt}   Disagree {c |}{res}        214       26.03      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        822      100.00
{txt}
{com}.                 tab d2_voted 

    {txt}D2 Vote {c |}
Disagreemen {c |}
          t {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
      Agree {c |}{res}        447       72.33       72.33
{txt}   Disagree {c |}{res}        171       27.67      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        618      100.00
{txt}
{com}.                 tab d3_voted

    {txt}D3 Vote {c |}
Disagreemen {c |}
          t {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
      Agree {c |}{res}        286       71.50       71.50
{txt}   Disagree {c |}{res}        114       28.50      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        400      100.00
{txt}
{com}.                 tab d4_voted

    {txt}D4 Vote {c |}
Disagreemen {c |}
          t {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
      Agree {c |}{res}        163       69.96       69.96
{txt}   Disagree {c |}{res}         70       30.04      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        233      100.00
{txt}
{com}.                 
.         
. *Exposure to Disagreement
.         *Agreement*
.                 egen cand_agree = rowtotal(d1_votea d2_votea d3_votea d4_votea), missing
{txt}(917 missing values generated)

{com}.         *Disagreeement                          
.                 egen cand_disagree = rowtotal(d1_voted d2_voted d3_voted d4_voted), missing
{txt}(917 missing values generated)

{com}.                 
.                 tab cand_agree

 {txt}cand_agree {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        117       13.15       13.15
{txt}          1 {c |}{res}        297       33.37       46.52
{txt}          2 {c |}{res}        286       32.13       78.65
{txt}          3 {c |}{res}        125       14.04       92.70
{txt}          4 {c |}{res}         65        7.30      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        890      100.00
{txt}
{com}.                 tab cand_disagree

{txt}cand_disagr {c |}
         ee {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        496       55.73       55.73
{txt}          1 {c |}{res}        259       29.10       84.83
{txt}          2 {c |}{res}         99       11.12       95.96
{txt}          3 {c |}{res}         32        3.60       99.55
{txt}          4 {c |}{res}          4        0.45      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        890      100.00
{txt}
{com}.                 summ cand_agree cand_disagree

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 2}cand_agree {c |}{res}       890    1.689888    1.094217          0          4
{txt}cand_disag~e {c |}{res}       890    .6393258    .8506093          0          4
{txt}
{com}.                 
.         *Difference*
.                 *Summary Scale
.                         *See Lupton and Thornton: Exposure = D - A
.                                 gen disagree_total = cand_disagree - cand_agree
{txt}(917 missing values generated)

{com}.                                 label var disagree_total "Network Disagreement"
{txt}
{com}.                 *Corrected for # D & A
.                         *See Lupton & Thornton: (D-A)/(D+A)
.                                 gen disagree_avg = [cand_disagree - cand_agree] / [cand_disagree + cand_agree]
{txt}(917 missing values generated)

{com}.                                 label var disagree_avg "Network Disagreement"
{txt}
{com}.         
.                 *Standardized*
.                         foreach var in disagree_total disagree_avg {c -(}
{txt}  2{com}.                                 summ `var'
{txt}  3{com}.                                 gen `var'01 = (`var' - r(min))/(r(max)-r(min))
{txt}  4{com}.                         {c )-}

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
disagree_t~l {c |}{res}       890   -1.050562    1.631635         -4          4
{txt}(917 missing values generated)

    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
disagree_avg {c |}{res}       890    -.452809    .7176042         -1          1
{txt}(917 missing values generated)

{com}.                         
.                         label var disagree_total01 "Network Disagreement"
{txt}
{com}.                         label var disagree_avg01 "Network Disagreement"
{txt}
{com}.                                 
. 
. *Network Diversity
.         *From Nir: [(Agree+Disagree)/2] - |A-D|
.                 gen network_ambiv = [(cand_agree + cand_disagree)/2] - abs(cand_agree - cand_disagree)
{txt}(917 missing values generated)

{com}.                 label var network_ambiv "Network Political Diversity"
{txt}
{com}.                 
.                 summ network_ambiv

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
network_am~v {c |}{res}       890   -.4477528    .9678874         -2          2
{txt}
{com}.                 gen network_ambiv01=(network_ambiv - r(min))/(r(max)-r(min))
{txt}(917 missing values generated)

{com}.                 label var network_ambiv01 "Network Political Diversity"
{txt}
{com}. 
.                         
. 
.                 ***************************************************
.                 *****************Control Variables*****************
.                 ***************************************************
.                 
.         
. *Education
.         recode V000913 (1=1) (2=1) (3=2) (4=3) (5=3) (6=4) (7=4) (9=.), gen(educ)
{txt}(1743 differences between V000913 and educ)

{com}.         tab V000913 educ

                      {txt}{c |}   RECODE of V000913 (Y3x. R educ summary)
  Y3x. R educ summary {c |}         1          2          3          4 {c |}     Total
{hline 22}{c +}{hline 44}{c +}{hline 10}
1. 8 grades or less a {c |}{res}        64          0          0          0 {txt}{c |}{res}        64 
{txt}2. 9-11 grades, no fu {c |}{res}       116          0          0          0 {txt}{c |}{res}       116 
{txt}3. High school diplom {c |}{res}         0        519          0          0 {txt}{c |}{res}       519 
{txt}4. More than 12 years {c |}{res}         0          0        377          0 {txt}{c |}{res}       377 
{txt}5. Junior or communit {c |}{res}         0          0        168          0 {txt}{c |}{res}       168 
{txt}6. BA level degrees.  {c |}{res}         0          0          0        373 {txt}{c |}{res}       373 
{txt}7. Advanced degree, i {c |}{res}         0          0          0        183 {txt}{c |}{res}       183 
{txt}{hline 22}{c +}{hline 44}{c +}{hline 10}
                Total {c |}{res}       180        519        545        556 {txt}{c |}{res}     1,800 

{txt}
{com}.         label var educ "Education"
{txt}
{com}.         label def edu 1 "< HS" 2 "HS" 3 "Some College" 4 "College Degree+"
{txt}
{com}.         label values educ edu
{txt}
{com}.         tab educ

      {txt}Education {c |}      Freq.     Percent        Cum.
{hline 16}{c +}{hline 35}
           < HS {c |}{res}        180       10.00       10.00
{txt}             HS {c |}{res}        519       28.83       38.83
{txt}   Some College {c |}{res}        545       30.28       69.11
{txt}College Degree+ {c |}{res}        556       30.89      100.00
{txt}{hline 16}{c +}{hline 35}
          Total {c |}{res}      1,800      100.00
{txt}
{com}.         
.         summ educ 

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 8}educ {c |}{res}      1800    2.820556    .9826283          1          4
{txt}
{com}.         gen educ01 = (educ - r(min))/(r(max)-r(min))
{txt}(7 missing values generated)

{com}.         label var educ01 "Education"
{txt}
{com}. 
. 
. *Income
.         rename V000994 income
{res}{txt}
{com}.         label var income "Household Income"
{txt}
{com}.         mvdecode income, mv(00 = . \ 0 = . \ 98 =. \ 99 = .)
      {txt}income:{res}{col 15}292{txt} missing values generated

{com}.         summ income, detail

                      {txt}Household Income
{hline 61}
      Percentiles      Smallest
 1%    {res}        1              1
{txt} 5%    {res}        2              1
{txt}10%    {res}        3              1       {txt}Obs         {res}       1515
{txt}25%    {res}        4              1       {txt}Sum of Wgt. {res}       1515

{txt}50%    {res}        6                      {txt}Mean          {res} 6.763696
                        {txt}Largest       Std. Dev.     {res} 3.748659
{txt}75%    {res}        8             22
{txt}90%    {res}       11             22       {txt}Variance      {res} 14.05245
{txt}95%    {res}       14             22       {txt}Skewness      {res} 1.408068
{txt}99%    {res}       21             22       {txt}Kurtosis      {res} 5.924098
{txt}
{com}.         tab income

              {txt}Household Income {c |}      Freq.     Percent        Cum.
{hline 31}{c +}{hline 35}
1. A. NONE OR LESS THAN $4,999 {c |}{res}         48        3.17        3.17
{txt}           2. B. $5,000-$9,999 {c |}{res}         81        5.35        8.51
{txt}         3. C. $10,000-$14,999 {c |}{res}        102        6.73       15.25
{txt}         4. D. $15,000-$24,999 {c |}{res}        174       11.49       26.73
{txt}         5. E. $25,000-$34,999 {c |}{res}        199       13.14       39.87
{txt}         6. F. $35,000-$49,999 {c |}{res}        238       15.71       55.58
{txt}         7. G. $50,000-$64,999 {c |}{res}        200       13.20       68.78
{txt}         8. H. $65,000-$74,999 {c |}{res}        122        8.05       76.83
{txt}         9. J. $75,000-$84,999 {c |}{res}         94        6.20       83.04
{txt}        10. K. $85,000-$94,999 {c |}{res}         61        4.03       87.06
{txt}       11. M. $95,000-$104,999 {c |}{res}         50        3.30       90.36
{txt}      12. N. $105,000-$114,999 {c |}{res}         36        2.38       92.74
{txt}      13. P. $115,000-$124,999 {c |}{res}         24        1.58       94.32
{txt}      14. Q. $125,000-$134,999 {c |}{res}         17        1.12       95.45
{txt}      15. R. $135,000-$144,999 {c |}{res}         12        0.79       96.24
{txt}      16. S. $145,000-$154,999 {c |}{res}         13        0.86       97.10
{txt}      17. T. $155,000-$164,999 {c |}{res}         11        0.73       97.82
{txt}      18. U. $165,000-$174,999 {c |}{res}          7        0.46       98.28
{txt}      19. V. $175,000-$184,999 {c |}{res}          6        0.40       98.68
{txt}      20. W. $185,000-$194,999 {c |}{res}          1        0.07       98.75
{txt}      21. X. $195,000-$199,999 {c |}{res}          5        0.33       99.08
{txt}      22. Y. $200,000 and over {c |}{res}         14        0.92      100.00
{txt}{hline 31}{c +}{hline 35}
                         Total {c |}{res}      1,515      100.00
{txt}
{com}.         
.         summ income

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 6}income {c |}{res}      1515    6.763696    3.748659          1         22
{txt}
{com}.         gen income01 = (income - r(min))/(r(max)-r(min))
{txt}(292 missing values generated)

{com}.         label var income01 "Household Income"
{txt}
{com}. 
. 
. *Employment Status
.         recode  V000918 (0=.) (1=1) (2=2) (3=2) (4=3) (5=4) (6=5) (7=6), gen(employment)
{txt}(755 differences between V000918 and employment)

{com}.         label var employment "Employment Status"
{txt}
{com}.         label def emp 1 "Working" 2 "Unemployed" 3 "Retired" 4 "Perm. Disabled" 5 "Homemaker" 6 "Student" 
{txt}
{com}.         label values employment emp
{txt}
{com}.         
.         recode employment (1=1) (2=2) (3=3) (4=4) (5=4) (6=4), gen(employed)
{txt}(279 differences between employment and employed)

{com}.         label var employed "Employment Status"
{txt}
{com}.         label def emp1 1 "Working" 2 "Unemployed" 3 "Retired" 4 "Perm. Disabled/Homemaker/Student"
{txt}
{com}.         label values employed emp1
{txt}
{com}. 
. *Age
.         rename  V000908 age
{res}{txt}
{com}.         mvdecode age, mv(00 = . \ 0 = . )
         {txt}age:{res}{col 15}9{txt} missing values generated

{com}.         label var age "Age"
{txt}
{com}.         tab age

             {txt}Age {c |}      Freq.     Percent        Cum.
{hline 17}{c +}{hline 35}
              18 {c |}{res}         12        0.67        0.67
{txt}              19 {c |}{res}         16        0.89        1.56
{txt}              20 {c |}{res}         23        1.28        2.84
{txt}              21 {c |}{res}         23        1.28        4.12
{txt}              22 {c |}{res}         23        1.28        5.39
{txt}              23 {c |}{res}         23        1.28        6.67
{txt}              24 {c |}{res}         26        1.45        8.12
{txt}              25 {c |}{res}         26        1.45        9.57
{txt}              26 {c |}{res}         28        1.56       11.12
{txt}              27 {c |}{res}         28        1.56       12.68
{txt}              28 {c |}{res}         29        1.61       14.29
{txt}              29 {c |}{res}         22        1.22       15.52
{txt}              30 {c |}{res}         34        1.89       17.41
{txt}              31 {c |}{res}         30        1.67       19.08
{txt}              32 {c |}{res}         36        2.00       21.08
{txt}              33 {c |}{res}         33        1.84       22.91
{txt}              34 {c |}{res}         40        2.22       25.14
{txt}              35 {c |}{res}         41        2.28       27.42
{txt}              36 {c |}{res}         45        2.50       29.92
{txt}              37 {c |}{res}         48        2.67       32.59
{txt}              38 {c |}{res}         40        2.22       34.82
{txt}              39 {c |}{res}         44        2.45       37.26
{txt}              40 {c |}{res}         46        2.56       39.82
{txt}              41 {c |}{res}         42        2.34       42.16
{txt}              42 {c |}{res}         48        2.67       44.83
{txt}              43 {c |}{res}         37        2.06       46.89
{txt}              44 {c |}{res}         41        2.28       49.17
{txt}              45 {c |}{res}         34        1.89       51.06
{txt}              46 {c |}{res}         35        1.95       53.00
{txt}              47 {c |}{res}         38        2.11       55.12
{txt}              48 {c |}{res}         36        2.00       57.12
{txt}              49 {c |}{res}         32        1.78       58.90
{txt}              50 {c |}{res}         39        2.17       61.07
{txt}              51 {c |}{res}         35        1.95       63.01
{txt}              52 {c |}{res}         26        1.45       64.46
{txt}              53 {c |}{res}         31        1.72       66.18
{txt}              54 {c |}{res}         30        1.67       67.85
{txt}              55 {c |}{res}         26        1.45       69.30
{txt}              56 {c |}{res}         36        2.00       71.30
{txt}              57 {c |}{res}         40        2.22       73.53
{txt}              58 {c |}{res}         33        1.84       75.36
{txt}              59 {c |}{res}         25        1.39       76.75
{txt}              60 {c |}{res}         21        1.17       77.92
{txt}              61 {c |}{res}         20        1.11       79.03
{txt}              62 {c |}{res}         17        0.95       79.98
{txt}              63 {c |}{res}         29        1.61       81.59
{txt}              64 {c |}{res}         16        0.89       82.48
{txt}              65 {c |}{res}         24        1.33       83.82
{txt}              66 {c |}{res}         17        0.95       84.76
{txt}              67 {c |}{res}         14        0.78       85.54
{txt}              68 {c |}{res}         17        0.95       86.48
{txt}              69 {c |}{res}         18        1.00       87.49
{txt}              70 {c |}{res}         17        0.95       88.43
{txt}              71 {c |}{res}         11        0.61       89.04
{txt}              72 {c |}{res}         16        0.89       89.93
{txt}              73 {c |}{res}         22        1.22       91.16
{txt}              74 {c |}{res}         17        0.95       92.10
{txt}              75 {c |}{res}         17        0.95       93.05
{txt}              76 {c |}{res}         14        0.78       93.83
{txt}              77 {c |}{res}          9        0.50       94.33
{txt}              78 {c |}{res}         12        0.67       94.99
{txt}              79 {c |}{res}         13        0.72       95.72
{txt}              80 {c |}{res}         10        0.56       96.27
{txt}              81 {c |}{res}          9        0.50       96.77
{txt}              82 {c |}{res}         15        0.83       97.61
{txt}              83 {c |}{res}          6        0.33       97.94
{txt}              84 {c |}{res}          9        0.50       98.44
{txt}              85 {c |}{res}          5        0.28       98.72
{txt}              86 {c |}{res}          1        0.06       98.78
{txt}              87 {c |}{res}          2        0.11       98.89
{txt}              88 {c |}{res}          6        0.33       99.22
{txt}              89 {c |}{res}          5        0.28       99.50
{txt}              90 {c |}{res}          2        0.11       99.61
{txt}              91 {c |}{res}          1        0.06       99.67
{txt}              92 {c |}{res}          1        0.06       99.72
{txt}              93 {c |}{res}          2        0.11       99.83
{txt}              95 {c |}{res}          1        0.06       99.89
{txt}              96 {c |}{res}          1        0.06       99.94
{txt}97. 97 and older {c |}{res}          1        0.06      100.00
{txt}{hline 17}{c +}{hline 35}
           Total {c |}{res}      1,798      100.00
{txt}
{com}.         summ age, detail

                             {txt}Age
{hline 61}
      Percentiles      Smallest
 1%    {res}       19             18
{txt} 5%    {res}       22             18
{txt}10%    {res}       26             18       {txt}Obs         {res}       1798
{txt}25%    {res}       34             18       {txt}Sum of Wgt. {res}       1798

{txt}50%    {res}       45                      {txt}Mean          {res} 47.20634
                        {txt}Largest       Std. Dev.     {res} 16.96201
{txt}75%    {res}       58             93
{txt}90%    {res}       73             95       {txt}Variance      {res} 287.7098
{txt}95%    {res}       79             96       {txt}Skewness      {res} .4333556
{txt}99%    {res}       88             97       {txt}Kurtosis      {res} 2.475914
{txt}
{com}.         
.         summ age

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 9}age {c |}{res}      1798    47.20634    16.96201         18         97
{txt}
{com}.         gen age01 = (age - r(min))/(r(max)-r(min))
{txt}(9 missing values generated)

{com}.         label var age01 "Age"
{txt}
{com}.         
.         
. *Gender
.         recode V001029 (1=0) (2=1), gen(gender)
{txt}(1807 differences between V001029 and gender)

{com}.         label var gender "Gender"
{txt}
{com}.         label def gend 1 "Female" 0 "Male"
{txt}
{com}.         label values gender gend
{txt}
{com}.         tab gender

     {txt}Gender {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       Male {c |}{res}        790       43.72       43.72
{txt}     Female {c |}{res}      1,017       56.28      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,807      100.00
{txt}
{com}.         
. *Personal Financial Situation
.         *Better Worse Off From 1 Year Ago
.                 recode  V000401 (1=5) (2=4) (3=3) (4=2) (5=1) (0=.) (8=.) (9=.), gen(retf_pre)
{txt}(1325 differences between V000401 and retf_pre)

{com}.                 recode  V001412 (1=5) (2=4) (3=3) (4=2) (5=1) (0=.) (8=.) (9=.), gen(retf_post)
{txt}(1372 differences between V001412 and retf_post)

{com}.                 gen retro_finance = .
{txt}(1807 missing values generated)

{com}.                 replace retro_finance = retf_pre
{txt}(923 real changes made)

{com}.                 replace retro_finance = retf_post if retro_finance == .
{txt}(761 real changes made)

{com}.                 label var retro_finance "Finances Compraed to Yr. Ago"
{txt}
{com}.                 label values retro_finance eco3 
{txt}
{com}.         
.         *Dental/Medical Care
.                 recode V000402 (1=1) (5=0) (9=.) (0=.), gen(med_pre)
{txt}(1529 differences between V000402 and med_pre)

{com}.                 recode V001413 (1=1) (5=0) (9=.) (0=.), gen(med_post)
{txt}(1628 differences between V001413 and med_post)

{com}.                 gen medical = .
{txt}(1807 missing values generated)

{com}.                 replace medical = med_pre
{txt}(927 real changes made)

{com}.                 replace medical = med_post if medical == . 
{txt}(764 real changes made)

{com}.                 label var medical "Put off medical care?"
{txt}
{com}.                 label def med 1 "Put Off Medical Care" 0 "Did Not Put Off Med. Care"
{txt}
{com}.                 label values medical med
{txt}
{com}.         
.         *Better Worse Off in 1 year
.                 recode V000406 (1=5) (2=4) (3=3) (4=2) (5=1) (0=.) (8=.) (9=.), gen(prosp_pre)
{txt}(1328 differences between V000406 and prosp_pre)

{com}.                 recode V001417 (1=5) (2=4) (3=3) (4=2) (5=1) (0=.) (8=.) (9=.), gen(prosp_post)
{txt}(1393 differences between V001417 and prosp_post)

{com}.                 gen prosp_finance = . 
{txt}(1807 missing values generated)

{com}.                 replace prosp_finance = prosp_pre
{txt}(898 real changes made)

{com}.                 replace prosp_finance = prosp_post if prosp_finance == . 
{txt}(744 real changes made)

{com}.                 label var prosp_finance "Exp. Finances in 1 Yr"
{txt}
{com}.                 label values prosp_finance eco3 
{txt}
{com}.                 
.         *Summary Statisitcs and Inter-relationship
.                 tab retro_finance 

       {txt}Finances {c |}
Compraed to Yr. {c |}
            Ago {c |}      Freq.     Percent        Cum.
{hline 16}{c +}{hline 35}
     Much Worse {c |}{res}         64        3.80        3.80
{txt} Somewhat Worse {c |}{res}        145        8.61       12.41
{txt}      No Effect {c |}{res}        917       54.45       66.86
{txt}Somewhat Better {c |}{res}        375       22.27       89.13
{txt}    Much Better {c |}{res}        183       10.87      100.00
{txt}{hline 16}{c +}{hline 35}
          Total {c |}{res}      1,684      100.00
{txt}
{com}.                 tab medical

    {txt}Put off medical care? {c |}      Freq.     Percent        Cum.
{hline 26}{c +}{hline 35}
Did Not Put Off Med. Care {c |}{res}      1,234       72.97       72.97
{txt}     Put Off Medical Care {c |}{res}        457       27.03      100.00
{txt}{hline 26}{c +}{hline 35}
                    Total {c |}{res}      1,691      100.00
{txt}
{com}.                 tab prosp_finance

  {txt}Exp. Finances {c |}
        in 1 Yr {c |}      Freq.     Percent        Cum.
{hline 16}{c +}{hline 35}
     Much Worse {c |}{res}         24        1.46        1.46
{txt} Somewhat Worse {c |}{res}         70        4.26        5.72
{txt}      No Effect {c |}{res}        893       54.38       60.11
{txt}Somewhat Better {c |}{res}        451       27.47       87.58
{txt}    Much Better {c |}{res}        204       12.42      100.00
{txt}{hline 16}{c +}{hline 35}
          Total {c |}{res}      1,642      100.00
{txt}
{com}.                 
.                 pwcorr retro_finance prosp_finance, sig

             {txt}{c |} retro_~e prosp~ce
{hline 13}{c +}{hline 18}
retro_fina~e {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
prosp_fina~e {c |} {res}  0.3000   1.0000 
             {txt}{c |}{res}   0.0000
             {txt}{c |}

{com}.                 tab retro_finance medical, row col chi2
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

       Finances {c |}
Compraed to Yr. {c |} Put off medical care?
            Ago {c |} Did Not P  Put Off M {c |}     Total
{hline 16}{c +}{hline 22}{c +}{hline 10}
     Much Worse {c |}{res}        32         32 {txt}{c |}{res}        64 
                {txt}{c |}{res}     50.00      50.00 {txt}{c |}{res}    100.00 
                {txt}{c |}{res}      2.60       7.03 {txt}{c |}{res}      3.80 
{txt}{hline 16}{c +}{hline 22}{c +}{hline 10}
 Somewhat Worse {c |}{res}        88         57 {txt}{c |}{res}       145 
                {txt}{c |}{res}     60.69      39.31 {txt}{c |}{res}    100.00 
                {txt}{c |}{res}      7.16      12.53 {txt}{c |}{res}      8.61 
{txt}{hline 16}{c +}{hline 22}{c +}{hline 10}
      No Effect {c |}{res}       678        239 {txt}{c |}{res}       917 
                {txt}{c |}{res}     73.94      26.06 {txt}{c |}{res}    100.00 
                {txt}{c |}{res}     55.17      52.53 {txt}{c |}{res}     54.45 
{txt}{hline 16}{c +}{hline 22}{c +}{hline 10}
Somewhat Better {c |}{res}       292         83 {txt}{c |}{res}       375 
                {txt}{c |}{res}     77.87      22.13 {txt}{c |}{res}    100.00 
                {txt}{c |}{res}     23.76      18.24 {txt}{c |}{res}     22.27 
{txt}{hline 16}{c +}{hline 22}{c +}{hline 10}
    Much Better {c |}{res}       139         44 {txt}{c |}{res}       183 
                {txt}{c |}{res}     75.96      24.04 {txt}{c |}{res}    100.00 
                {txt}{c |}{res}     11.31       9.67 {txt}{c |}{res}     10.87 
{txt}{hline 16}{c +}{hline 22}{c +}{hline 10}
          Total {c |}{res}     1,229        455 {txt}{c |}{res}     1,684 
                {txt}{c |}{res}     72.98      27.02 {txt}{c |}{res}    100.00 
                {txt}{c |}{res}    100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}4{txt}) = {res} 34.0362  {txt} Pr = {res}0.000
{txt}
{com}.                 tab prosp_finance medical, row col chi2
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

  Exp. Finances {c |} Put off medical care?
        in 1 Yr {c |} Did Not P  Put Off M {c |}     Total
{hline 16}{c +}{hline 22}{c +}{hline 10}
     Much Worse {c |}{res}        12         12 {txt}{c |}{res}        24 
                {txt}{c |}{res}     50.00      50.00 {txt}{c |}{res}    100.00 
                {txt}{c |}{res}      1.00       2.72 {txt}{c |}{res}      1.46 
{txt}{hline 16}{c +}{hline 22}{c +}{hline 10}
 Somewhat Worse {c |}{res}        44         26 {txt}{c |}{res}        70 
                {txt}{c |}{res}     62.86      37.14 {txt}{c |}{res}    100.00 
                {txt}{c |}{res}      3.66       5.90 {txt}{c |}{res}      4.26 
{txt}{hline 16}{c +}{hline 22}{c +}{hline 10}
      No Effect {c |}{res}       678        215 {txt}{c |}{res}       893 
                {txt}{c |}{res}     75.92      24.08 {txt}{c |}{res}    100.00 
                {txt}{c |}{res}     56.45      48.75 {txt}{c |}{res}     54.38 
{txt}{hline 16}{c +}{hline 22}{c +}{hline 10}
Somewhat Better {c |}{res}       324        127 {txt}{c |}{res}       451 
                {txt}{c |}{res}     71.84      28.16 {txt}{c |}{res}    100.00 
                {txt}{c |}{res}     26.98      28.80 {txt}{c |}{res}     27.47 
{txt}{hline 16}{c +}{hline 22}{c +}{hline 10}
    Much Better {c |}{res}       143         61 {txt}{c |}{res}       204 
                {txt}{c |}{res}     70.10      29.90 {txt}{c |}{res}    100.00 
                {txt}{c |}{res}     11.91      13.83 {txt}{c |}{res}     12.42 
{txt}{hline 16}{c +}{hline 22}{c +}{hline 10}
          Total {c |}{res}     1,201        441 {txt}{c |}{res}     1,642 
                {txt}{c |}{res}     73.14      26.86 {txt}{c |}{res}    100.00 
                {txt}{c |}{res}    100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}4{txt}) = {res} 15.1814  {txt} Pr = {res}0.004
{txt}
{com}.                 ttest retro_finance, by(medical)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
 Did Not {c |}{res}{col 12}   1229{col 22} 3.340114{col 34} .0247144{col 46} .8664167{col 58} 3.291627{col 70} 3.388601
 {txt}Put Off {c |}{res}{col 12}    455{col 22}  3.10989{col 34} .0460738{col 46} .9827874{col 58} 3.019346{col 70} 3.200434
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}   1684{col 22}  3.27791{col 34} .0220497{col 46} .9048421{col 58} 3.234662{col 70} 3.321157
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .2302238{col 34} .0493514{col 58} .1334271{col 70} .3270205
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}Did Not{txt}) - mean({res}Put Off{txt})                          t = {res}  4.6650
{txt}Ho: diff = 0                                     degrees of freedom = {res}    1682

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000
{txt}
{com}.                 ttest prosp_finance, by(medical)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
 Did Not {c |}{res}{col 12}   1201{col 22} 3.451291{col 34} .0227117{col 46} .7870835{col 58} 3.406732{col 70}  3.49585
 {txt}Put Off {c |}{res}{col 12}    441{col 22} 3.451247{col 34}  .042785{col 46} .8984859{col 58} 3.367159{col 70} 3.535336
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}   1642{col 22} 3.451279{col 34}  .020192{col 46} .8182122{col 58} 3.411674{col 70} 3.490884
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .0000434{col 34} .0455716{col 58}-.0893412{col 70} .0894281
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}Did Not{txt}) - mean({res}Put Off{txt})                          t = {res}  0.0010
{txt}Ho: diff = 0                                     degrees of freedom = {res}    1640

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.5004         {txt}Pr(|T| > |t|) = {res}0.9992          {txt}Pr(T > t) = {res}0.4996
{txt}
{com}.                 
.                 
. *Race/Hispanic*
.         gen race = . 
{txt}(1807 missing values generated)

{com}.         replace race = 1 if V001006a == 50
{txt}(1393 real changes made)

{com}.         replace race = 2 if V001006a == 10
{txt}(208 real changes made)

{com}.         replace race = 3 if V001006a == 20
{txt}(32 real changes made)

{com}.         replace race = 3 if V001006a == 30
{txt}(20 real changes made)

{com}.         replace race = 3 if V001006a == 40
{txt}(93 real changes made)

{com}.         replace race = 3 if V001006a >= 60 & V001006a <= 90
{txt}(42 real changes made)

{com}.         label var race "Race"
{txt}
{com}.         label def rac 1 "White" 2 "Black" 3 "Other"
{txt}
{com}.         label values race rac
{txt}
{com}.         
.         gen race_eth = . 
{txt}(1807 missing values generated)

{com}.         replace race_eth = 1 if race == 1 & V001012 == 5
{txt}(1360 real changes made)

{com}.         replace race_eth = 2 if race == 2 & V001012 == 5
{txt}(203 real changes made)

{com}.         replace race_eth = 1 if race == 1 & V001012 == 8
{txt}(3 real changes made)

{com}.         replace race_eth = 2 if race == 2 & V001012 == 8
{txt}(0 real changes made)

{com}.         replace race_eth = 3 if race == 3 & V001012 == 1
{txt}(96 real changes made)

{com}.         replace race_eth = 4 if race == 3 & V001012 == 5
{txt}(88 real changes made)

{com}.         replace race_eth = 4 if race == 3 & V001012 == 8
{txt}(3 real changes made)

{com}.         label def rac1 1 "White" 2 "Black" 3 "Hispanic" 4 "Other" 
{txt}
{com}.         label values race_eth rac1
{txt}
{com}.         label var race_eth "Race/Ethnicity"
{txt}
{com}. 
. 
. *Marital Status
.         recode V000909 (0=.) (1=1) (2=0) (3=0) (4=0) (5=0) (6=0)(8=.) (9=.), gen(marital)
{txt}(872 differences between V000909 and marital)

{com}.         label var marital "Marital Status"
{txt}
{com}.         label def mar 1 "Married" 0 "Not Married" 
{txt}
{com}.         label values marital mar
{txt}
{com}.         
.         
. *Political Interest
.         tab V001367

 {txt}F5. R follows govt and public {c |}
                       affairs {c |}      Freq.     Percent        Cum.
{hline 31}{c +}{hline 35}
0. NA. INAP, no Post interview {c |}{res}        252       13.95       13.95
{txt}           1. MOST OF THE TIME {c |}{res}        339       18.76       32.71
{txt}           2. SOME OF THE TIME {c |}{res}        569       31.49       64.19
{txt}          3. ONLY NOW AND THEN {c |}{res}        423       23.41       87.60
{txt}              4. HARDLY AT ALL {c |}{res}        212       11.73       99.34
{txt}                         8. DK {c |}{res}         11        0.61       99.94
{txt}                         9. RF {c |}{res}          1        0.06      100.00
{txt}{hline 31}{c +}{hline 35}
                         Total {c |}{res}      1,807      100.00
{txt}
{com}.         recode V001367 (1=4) (2=3) (3=2) (4=1) (0=.) (8=.) (9=.), gen(interest)
{txt}(1807 differences between V001367 and interest)

{com}.         label var interest "Political Interest"
{txt}
{com}.         label def int 1 "Hardly at all" 2 "Only now and then" 3 "Some of the time" 4 "Most of the Time"
{txt}
{com}.         label values interest int
{txt}
{com}.         tab interest

        {txt}Political {c |}
         Interest {c |}      Freq.     Percent        Cum.
{hline 18}{c +}{hline 35}
    Hardly at all {c |}{res}        212       13.74       13.74
{txt}Only now and then {c |}{res}        423       27.41       41.15
{txt} Some of the time {c |}{res}        569       36.88       78.03
{txt} Most of the Time {c |}{res}        339       21.97      100.00
{txt}{hline 18}{c +}{hline 35}
            Total {c |}{res}      1,543      100.00
{txt}
{com}.         summ interest, detail

                     {txt}Political Interest
{hline 61}
      Percentiles      Smallest
 1%    {res}        1              1
{txt} 5%    {res}        1              1
{txt}10%    {res}        1              1       {txt}Obs         {res}       1543
{txt}25%    {res}        2              1       {txt}Sum of Wgt. {res}       1543

{txt}50%    {res}        3                      {txt}Mean          {res} 2.670771
                        {txt}Largest       Std. Dev.     {res} .9672832
{txt}75%    {res}        3              4
{txt}90%    {res}        4              4       {txt}Variance      {res} .9356367
{txt}95%    {res}        4              4       {txt}Skewness      {res}-.2150036
{txt}99%    {res}        4              4       {txt}Kurtosis      {res}  2.07749
{txt}
{com}.         
.         summ interest

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 4}interest {c |}{res}      1543    2.670771    .9672832          1          4
{txt}
{com}.         gen interest01 = (interest - r(min))/(r(max)-r(min))
{txt}(264 missing values generated)

{com}.         label var interest01 "Political Interest"
{txt}
{com}. 
. *News Attention
.         recode V001428 (0=.) (1=0) (3=0) (5=0) (7=0) (6=1) (8=.), gen(nightly)
{txt}(1807 differences between V001428 and nightly)

{com}.         label var nightly "Nightly News Watcher?"
{txt}
{com}.         label def nig 1 "No Nightly News" 0 "Nightly News" 
{txt}
{com}.         label values nightly nig
{txt}
{com}.         tab nightly

   {txt}Nightly News {c |}
       Watcher? {c |}      Freq.     Percent        Cum.
{hline 16}{c +}{hline 35}
   Nightly News {c |}{res}      1,327       87.82       87.82
{txt}No Nightly News {c |}{res}        184       12.18      100.00
{txt}{hline 16}{c +}{hline 35}
          Total {c |}{res}      1,511      100.00
{txt}
{com}.         
.         gen radio = . 
{txt}(1807 missing values generated)

{com}.         replace radio = 1 if V001430  == 5
{txt}(994 real changes made)

{com}.         replace radio = 2 if V001430 == 1 &  V001431 == 4
{txt}(248 real changes made)

{com}.         replace radio = 3 if V001430 == 1 &  V001431 == 3
{txt}(144 real changes made)

{com}.         replace radio = 4 if V001430 == 1 &  V001431 == 2
{txt}(77 real changes made)

{com}.         replace radio = 5 if V001430 == 1 &  V001431 == 1
{txt}(92 real changes made)

{com}.         label var radio "Listen to Talk Radio"
{txt}
{com}.         label def rad 1 "No" 2 "Ocassionally" 3 "1-2 Times Week" 4 "Most Days" 5 "Everyday"
{txt}
{com}.         label values radio rad
{txt}
{com}.         tab radio

{txt}Listen to Talk {c |}
         Radio {c |}      Freq.     Percent        Cum.
{hline 15}{c +}{hline 35}
            No {c |}{res}        994       63.92       63.92
{txt}  Ocassionally {c |}{res}        248       15.95       79.87
{txt}1-2 Times Week {c |}{res}        144        9.26       89.13
{txt}     Most Days {c |}{res}         77        4.95       94.08
{txt}      Everyday {c |}{res}         92        5.92      100.00
{txt}{hline 15}{c +}{hline 35}
         Total {c |}{res}      1,555      100.00
{txt}
{com}.         summ radio

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 7}radio {c |}{res}      1555    1.729904    1.179119          1          5
{txt}
{com}.         
.         gen internet = . 
{txt}(1807 missing values generated)

{com}.         replace internet = 1 if V001433 == 5
{txt}(581 real changes made)

{com}.         replace internet = 2 if V001433 == 1 & V001434 == 5
{txt}(509 real changes made)

{com}.         replace internet = 3 if V001433 == 1 & V001434 == 1
{txt}(464 real changes made)

{com}.         label var internet "Internet Access & Campaign Attention"
{txt}
{com}.         label def int1 1 "No Internet" 2 "Internet, No Election Info" 3 "Internet, Election Info"
{txt}
{com}.         label values internet int1
{txt}
{com}.         tab internet

{txt}Internet Access & Campaign {c |}
                 Attention {c |}      Freq.     Percent        Cum.
{hline 27}{c +}{hline 35}
               No Internet {c |}{res}        581       37.39       37.39
{txt}Internet, No Election Info {c |}{res}        509       32.75       70.14
{txt}   Internet, Election Info {c |}{res}        464       29.86      100.00
{txt}{hline 27}{c +}{hline 35}
                     Total {c |}{res}      1,554      100.00
{txt}
{com}.         
. *Knowledge
.         label def corre 1 "Correct" 0 "Incorrect/No Guess"
{txt}
{com}.         *lott 
.                 recode V001447 (0=.) (1=1) (5=0) (8=0) (9=.), gen(lott)
{txt}(1672 differences between V001447 and lott)

{com}.                 label var lott "Trent Lott Knowledge"
{txt}
{com}.                 label values lott corre
{txt}
{com}.         *reinquist
.                 recode V001450 (0=.) (1=1) (5=0) (8=0) (9=.), gen(rein)
{txt}(1643 differences between V001450 and rein)

{com}.                 label var rein "Reinquist Knowledge" 
{txt}
{com}.                 label values rein corre
{txt}
{com}.         *Blair
.                 recode V001453 (0=.) (1=1) (5=0) (8=0) (9=.), gen(blair)
{txt}(1271 differences between V001453 and blair)

{com}.                 label var blair "Blair Knowledge"
{txt}
{com}.                 label values blair corre
{txt}
{com}.         *Reno
.                 recode V001456 (0=.) (1=1) (5=0) (8=0) (9=.), gen(reno)
{txt}(950 differences between V001456 and reno)

{com}.                 label var reno "Reno Knowledge"
{txt}
{com}.                 label values reno corre
{txt}
{com}.         *bush state: K3a
.                 recode V001458 (0=.) (1=0) (2=0) (3=1) (4=0) (7=0) (8=0) (9=.), gen(bush_state)
{txt}(1807 differences between V001458 and bush_state)

{com}.                 label var bush_state "Bush State Knowledge"
{txt}
{com}.                 label values bush_state corre
{txt}
{com}.         *bush religion: K3b
.                 *correct = methodist*
.                 recode  V001460 (0=0) (1=0) (2=1) (3=0) (7=0) (8=0) (9=.), gen(bush_religion)
{txt}(1792 differences between V001460 and bush_religion)

{com}.                 label var bush_religion "Bush Religion Knowledge"
{txt}
{com}.                 label values bush_religion corre
{txt}
{com}.         *gore state k4a
.                 *correct = tennessee
.                 recode V001462 (0=.) (1=0) (2=1) (3=0) (4=0) (7=0) (8=0) (9=.), gen(gore_state)
{txt}(1807 differences between V001462 and gore_state)

{com}.                 label var gore_state "Gore State Knowledge"
{txt}
{com}.                 label values gore_state corre
{txt}
{com}.         *gore religion: k4b
.                 *correct = baptist
.                 recode V001464 (0=0) (1=1) (2=0) (3=0) (7=0) (8=0) (9=.), gen(gore_religion)
{txt}(1538 differences between V001464 and gore_religion)

{com}.                 label var gore_religion "Gore Religion Knowledge"
{txt}
{com}.                 label values gore_religion corre
{txt}
{com}.         *cheney state
.                 *correct = wyoming
.                 recode V001466 (0=.) (1=0) (2=0) (3=0) (4=1) (7=0) (8=0) (9=.), gen(cheney_state)
{txt}(1807 differences between V001466 and cheney_state)

{com}.                 label var cheney_state "Cheney State Knowledge"
{txt}
{com}.                 label values cheney_state corre
{txt}
{com}.         *cheney religion
.                 *correct = methodist*
.                 recode V001468 (0=0) (1=0) (2=1) (3=0) (7=0) (8=0) (9=.), gen(cheney_religion)
{txt}(1791 differences between V001468 and cheney_religion)

{com}.                 label var cheney_religion "Cheney Religion Knowledge"
{txt}
{com}.                 label values cheney_religion corre
{txt}
{com}.         *lieberman state
.                 *correct = ct
.                 recode V001470 (0=.) (1=1) (2=0) (3=0) (4=0) (7=0) (8=0) (9=.), gen(lieb_state)
{txt}(1334 differences between V001470 and lieb_state)

{com}.                 label var lieb_state "Liberman State Knowledge"
{txt}
{com}.                 label values lieb_state corre
{txt}
{com}.         *lieberman religion
.                 *correct = jewish
.                 recode V001472 (0=0) (1=0) (2=0) (3=1) (7=0) (8=0) (9=.), gen(lieb_religion)
{txt}(1805 differences between V001472 and lieb_religion)

{com}.                 label var lieb_religion "Liberman Religion Knowledge"
{txt}
{com}.                 label values lieb_religion corre
{txt}
{com}. 
. 
.         egen knowl = rowtotal(lott rein blair reno bush_state bush_religion gore_state gore_religion cheney_state cheney_religion lieb_religion lieb_state), missing
{txt}(252 missing values generated)

{com}.         label var knowl "Knowledge"
{txt}
{com}.         tab knowl

  {txt}Knowledge {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}         98        6.30        6.30
{txt}          1 {c |}{res}        159       10.23       16.53
{txt}          2 {c |}{res}        185       11.90       28.42
{txt}          3 {c |}{res}        223       14.34       42.77
{txt}          4 {c |}{res}        225       14.47       57.23
{txt}          5 {c |}{res}        203       13.05       70.29
{txt}          6 {c |}{res}        182       11.70       81.99
{txt}          7 {c |}{res}        123        7.91       89.90
{txt}          8 {c |}{res}         77        4.95       94.86
{txt}          9 {c |}{res}         64        4.12       98.97
{txt}         10 {c |}{res}         13        0.84       99.81
{txt}         11 {c |}{res}          3        0.19      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,555      100.00
{txt}
{com}.         summ knowl, detail

                          {txt}Knowledge
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        1              0       {txt}Obs         {res}       1555
{txt}25%    {res}        2              0       {txt}Sum of Wgt. {res}       1555

{txt}50%    {res}        4                      {txt}Mean          {res}  4.12926
                        {txt}Largest       Std. Dev.     {res} 2.462201
{txt}75%    {res}        6             10
{txt}90%    {res}        8             11       {txt}Variance      {res} 6.062432
{txt}95%    {res}        9             11       {txt}Skewness      {res} .2509962
{txt}99%    {res}       10             11       {txt}Kurtosis      {res} 2.348327
{txt}
{com}.         
.         summ knowl

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 7}knowl {c |}{res}      1555     4.12926    2.462201          0         11
{txt}
{com}.         gen knowl01 = (knowl - r(min))/(r(max)-r(min))
{txt}(252 missing values generated)

{com}.         label var knowl01 "Knowledge"
{txt}
{com}.                 
.                 
. *Ideology
.         tab  V000446

      {txt}G6x1. Summary self plcmnt lib-con {c |}
                                 scale/ {c |}      Freq.     Percent        Cum.
{hline 40}{c +}{hline 35}
                                  0. NA {c |}{res}          1        0.06        0.06
{txt}1. SCALE: 1 / BRANCHING: strong liberal {c |}{res}         82        4.54        4.59
{txt}2. SCALE: 2 / BRANCHING: not strong lib {c |}{res}        158        8.74       13.34
{txt}3. SCALE: 3. had to choose liberal / BR {c |}{res}        351       19.42       32.76
{txt}4. SCALE: 4. had to choose moderate/ BR {c |}{res}        109        6.03       38.79
{txt}5. SCALE: 5. had to choose conserv/ BRA {c |}{res}        528       29.22       68.01
{txt}6. SCALE: 6 / BRANCHING: not strong con {c |}{res}        250       13.84       81.85
{txt}7. SCALE: 7 / BRANCHING: strong conserv {c |}{res}        145        8.02       89.87
{txt}                                  8. DK {c |}{res}         18        1.00       90.87
{txt}                 9. R refuses to choose {c |}{res}        165        9.13      100.00
{txt}{hline 40}{c +}{hline 35}
                                  Total {c |}{res}      1,807      100.00
{txt}
{com}.         rename V000446 ideology
{res}{txt}
{com}.         
.         recode ideology (1 2 3 = 1) (5 6 7 = 2) (4 = 3) (0 8 9 = 4), gen(ideol_4)
{txt}(1725 differences between ideology and ideol_4)

{com}.         label var ideol_4 "Ideology"
{txt}
{com}.         label def ide 1 "Liberal" 2 "Conservative" 3 "Moderate" 4 "Refuse to Choose" 
{txt}
{com}.         label values ideol_4 ide
{txt}
{com}.         
.         mvdecode ideology, mv(0 = .a \ 8 = .b \ 9 = .c)
    {txt}ideology:{res}{col 15}184{txt} missing values generated

{com}.         tab ideology

      {txt}G6x1. Summary self plcmnt lib-con {c |}
                                 scale/ {c |}      Freq.     Percent        Cum.
{hline 40}{c +}{hline 35}
1. SCALE: 1 / BRANCHING: strong liberal {c |}{res}         82        5.05        5.05
{txt}2. SCALE: 2 / BRANCHING: not strong lib {c |}{res}        158        9.74       14.79
{txt}3. SCALE: 3. had to choose liberal / BR {c |}{res}        351       21.63       36.41
{txt}4. SCALE: 4. had to choose moderate/ BR {c |}{res}        109        6.72       43.13
{txt}5. SCALE: 5. had to choose conserv/ BRA {c |}{res}        528       32.53       75.66
{txt}6. SCALE: 6 / BRANCHING: not strong con {c |}{res}        250       15.40       91.07
{txt}7. SCALE: 7 / BRANCHING: strong conserv {c |}{res}        145        8.93      100.00
{txt}{hline 40}{c +}{hline 35}
                                  Total {c |}{res}      1,623      100.00
{txt}
{com}.         
.         summ ideology

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 4}ideology {c |}{res}      1623    4.338879    1.640956          1          7
{txt}
{com}.         gen ideology01 = (ideology - r(min))/(r(max)-r(min))
{txt}(184 missing values generated)

{com}.         label var ideology01 "Ideology"
{txt}
{com}.         
.         recode ideology (1=4) (2=3) (3=2) (4=1) (5=2) (6=3) (7=4), gen(ideol_str)
{txt}(1623 differences between ideology and ideol_str)

{com}.         label var ideol_str "Ideological Extremity"
{txt}
{com}.         
.         summ ideol_str

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 3}ideol_str {c |}{res}      1623    2.463956    .8143448          1          4
{txt}
{com}.         gen ideol_str01 = (ideol_str - r(min))/(r(max)-r(min))
{txt}(184 missing values generated)

{com}.         label var ideol_str01 "Ideological Extremity"
{txt}
{com}.         
.         
.         
.         
.         
. *Network Expertise
. 
. 
. foreach var in V001709 V001717 V001725 V001733 {c -(} 
{txt}  2{com}.                         tab `var'
{txt}  3{com}.                         {c )-}

    {txt}Z11. How much does name 1 {c |}
               know about pol {c |}      Freq.     Percent        Cum.
{hline 30}{c +}{hline 35}
0. NA. INAP, 5, 8, 9, 0 in Z1 {c |}{res}        655       36.25       36.25
{txt}              1. A GREAT DEAL {c |}{res}        483       26.73       62.98
{txt}         3. AN AVERAGE AMOUNT {c |}{res}        557       30.82       93.80
{txt}           5. NOT MUCH AT ALL {c |}{res}        104        5.76       99.56
{txt}          8. DK - DON'T PROBE {c |}{res}          7        0.39       99.94
{txt}          9. RF - DON'T PROBE {c |}{res}          1        0.06      100.00
{txt}{hline 30}{c +}{hline 35}
                        Total {c |}{res}      1,807      100.00

    {txt}Z15. How much does name 2 {c |}
               know about pol {c |}      Freq.     Percent        Cum.
{hline 30}{c +}{hline 35}
0. NA. INAP, 5, 8, 9, 0 in Z3 {c |}{res}        946       52.35       52.35
{txt}              1. A GREAT DEAL {c |}{res}        285       15.77       68.12
{txt}     3. AN AVERAGE AMOUNT, OR {c |}{res}        478       26.45       94.58
{txt}           5. NOT MUCH AT ALL {c |}{res}         94        5.20       99.78
{txt}          8. DK - DON'T PROBE {c |}{res}          3        0.17       99.94
{txt}          9. RF - DON'T PROBE {c |}{res}          1        0.06      100.00
{txt}{hline 30}{c +}{hline 35}
                        Total {c |}{res}      1,807      100.00

    {txt}Z19. How much does name 3 {c |}
               know about pol {c |}      Freq.     Percent        Cum.
{hline 30}{c +}{hline 35}
0. NA. INAP, 5, 8, 9, 0 in Z5 {c |}{res}      1,257       69.56       69.56
{txt}              1. A GREAT DEAL {c |}{res}        187       10.35       79.91
{txt}     3. AN AVERAGE AMOUNT, OR {c |}{res}        292       16.16       96.07
{txt}           5. NOT MUCH AT ALL {c |}{res}         69        3.82       99.89
{txt}          8. DK - DON'T PROBE {c |}{res}          2        0.11      100.00
{txt}{hline 30}{c +}{hline 35}
                        Total {c |}{res}      1,807      100.00

    {txt}Z23. How much does name 4 {c |}
               know about pol {c |}      Freq.     Percent        Cum.
{hline 30}{c +}{hline 35}
0. NA. INAP, 5, 8, 9, 0 in Z7 {c |}{res}      1,480       81.90       81.90
{txt}              1. A GREAT DEAL {c |}{res}        108        5.98       87.88
{txt}     3. AN AVERAGE AMOUNT, OR {c |}{res}        177        9.80       97.68
{txt}           5. NOT MUCH AT ALL {c |}{res}         42        2.32      100.00
{txt}{hline 30}{c +}{hline 35}
                        Total {c |}{res}      1,807      100.00
{txt}
{com}.                 recode V001709 (1=3) (3=2) (5=1), gen(disc1_knowl)
{txt}(1144 differences between V001709 and disc1_knowl)

{com}.                 recode V001717 (1=3) (3=2) (5=1), gen(disc2_knowl)
{txt}(857 differences between V001717 and disc2_knowl)

{com}.                 recode V001725 (1=3) (3=2) (5=1), gen(disc3_knowl)
{txt}(548 differences between V001725 and disc3_knowl)

{com}.                 recode V001733 (1=3) (3=2) (5=1), gen(disc4_knowl)
{txt}(327 differences between V001733 and disc4_knowl)

{com}.                 mvdecode disc1_knowl disc2_knowl disc3_knowl disc4_knowl, mv(0 = .a \ 8 = .b \ 9 = .c)
 {txt}disc1_knowl:{res}{col 15}663{txt} missing values generated
 disc2_knowl:{res}{col 15}950{txt} missing values generated
 disc3_knowl:{res}{col 15}1259{txt} missing values generated
 disc4_knowl:{res}{col 15}1480{txt} missing values generated

{com}.                 label def diknowl 1 "Not Much" 2 "Avg. Amount" 3 "Great Deal"
{txt}
{com}.                 foreach var in disc1_knowl disc2_knowl disc3_knowl disc4_knowl {c -(}
{txt}  2{com}.                         label values `var' diknowl
{txt}  3{com}.                         tab `var'
{txt}  4{com}.                         {c )-}

  {txt}RECODE of {c |}
    V001709 {c |}
  (Z11. How {c |}
  much does {c |}
name 1 know {c |}
 about pol) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
   Not Much {c |}{res}        104        9.09        9.09
{txt}Avg. Amount {c |}{res}        557       48.69       57.78
{txt} Great Deal {c |}{res}        483       42.22      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,144      100.00

  {txt}RECODE of {c |}
    V001717 {c |}
  (Z15. How {c |}
  much does {c |}
name 2 know {c |}
 about pol) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
   Not Much {c |}{res}         94       10.97       10.97
{txt}Avg. Amount {c |}{res}        478       55.78       66.74
{txt} Great Deal {c |}{res}        285       33.26      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        857      100.00

  {txt}RECODE of {c |}
    V001725 {c |}
  (Z19. How {c |}
  much does {c |}
name 3 know {c |}
 about pol) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
   Not Much {c |}{res}         69       12.59       12.59
{txt}Avg. Amount {c |}{res}        292       53.28       65.88
{txt} Great Deal {c |}{res}        187       34.12      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        548      100.00

  {txt}RECODE of {c |}
    V001733 {c |}
  (Z23. How {c |}
  much does {c |}
name 4 know {c |}
 about pol) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
   Not Much {c |}{res}         42       12.84       12.84
{txt}Avg. Amount {c |}{res}        177       54.13       66.97
{txt} Great Deal {c |}{res}        108       33.03      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        327      100.00
{txt}
{com}.                         
.                 egen disc_knowl = rowmean(disc1_knowl disc2_knowl disc3_knowl disc4_knowl)
{txt}(660 missing values generated)

{com}.                 label var disc_knowl "Network Pol. Know."
{txt}
{com}.                 summ disc_knowl

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 2}disc_knowl {c |}{res}      1147    2.257338    .5115385          1          3
{txt}
{com}.                 
.                 summ disc_knowl 

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 2}disc_knowl {c |}{res}      1147    2.257338    .5115385          1          3
{txt}
{com}.                 gen disc_knowl01 = (disc_knowl - r(min))/(r(max)-r(min))
{txt}(660 missing values generated)

{com}.                 label var disc_knowl01 "Network Pol. Knowledge"
{txt}
{com}.                 
.                 *Disagreement Scale Weighted by Disc Knowl*
.                         *Agree/Weight
.                         gen a1k = d1_votea * disc1_knowl 
{txt}(987 missing values generated)

{com}.                         gen a2k = d2_votea * disc2_knowl 
{txt}(1189 missing values generated)

{com}.                         gen a3k = d3_votea * disc3_knowl 
{txt}(1407 missing values generated)

{com}.                         gen a4k = d4_votea * disc4_knowl 
{txt}(1574 missing values generated)

{com}.                         egen agree_knowl = rowtotal(a1k a2k a3k a4k), missing
{txt}(917 missing values generated)

{com}.                 
.                         *Disagree/Weight
.                         gen d1k = d1_voted * disc1_knowl 
{txt}(987 missing values generated)

{com}.                         gen d2k = d2_voted * disc2_knowl 
{txt}(1189 missing values generated)

{com}.                         gen d3k = d3_voted * disc3_knowl 
{txt}(1407 missing values generated)

{com}.                         gen d4k = d4_voted * disc4_knowl 
{txt}(1574 missing values generated)

{com}.                         egen dagree_knowl = rowtotal(d1k d2k d3k d4k), missing
{txt}(917 missing values generated)

{com}.                 
.                         *Scale
.                                 gen disagree_total_knowl = dagree_knowl - agree_knowl
{txt}(917 missing values generated)

{com}.                                 gen disagree_avg_knowl = disagree_total_knowl/(cand_disagree + cand_agree)
{txt}(917 missing values generated)

{com}.                                 label var disagree_total_knowl "Network Disagreement (Knowledge Weighted)"
{txt}
{com}.                                 label var disagree_avg_knowl "Network Disagreement (Knowledge Weighted)"
{txt}
{com}.                                 
.                                 foreach var in disagree_total_knowl disagree_avg_knowl {c -(}
{txt}  2{com}.                                         summ `var'
{txt}  3{com}.                                         gen `var'01 = (`var' - r(min))/(r(max)-r(min))
{txt}  4{com}.                                         {c )-}

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
disa~l_knowl {c |}{res}       890    -2.55618    3.974849        -12         12
{txt}(917 missing values generated)

    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
disagree_a~l {c |}{res}       890   -1.094382    1.717809         -3          3
{txt}(917 missing values generated)

{com}.                                 
.                                 label var disagree_total_knowl01 "Network Disagreement (Knowledge Weighted)"
{txt}
{com}.                                 label var disagree_avg_knowl01 "Network Disagreement (Knowledge Weighted)"
{txt}
{com}.                                 
. 
. 
. *Freq. of Discussion
.                 
.         *When you talk with [fill name 1], do you discuss political matters�often, sometimes, rarely, or never?*
.         *1 = often; 3 = sometimes 5 = rarely 7 = never
.                 foreach var in  V001708 V001716 V001724 V001732 {c -(}
{txt}  2{com}.                         tab `var'
{txt}  3{com}.                         {c )-}

{txt}Z10. How often does R discuss {c |}
                   politics w {c |}      Freq.     Percent        Cum.
{hline 30}{c +}{hline 35}
0. NA. INAP, 5, 8, 9, 0 in Z1 {c |}{res}        655       36.25       36.25
{txt}                     1. OFTEN {c |}{res}        354       19.59       55.84
{txt}                 3. SOMETIMES {c |}{res}        593       32.82       88.66
{txt}                    5. RARELY {c |}{res}        195       10.79       99.45
{txt}                     7. NEVER {c |}{res}          6        0.33       99.78
{txt}                        8. DK {c |}{res}          2        0.11       99.89
{txt}                        9. RF {c |}{res}          2        0.11      100.00
{txt}{hline 30}{c +}{hline 35}
                        Total {c |}{res}      1,807      100.00

{txt}Z14. How often does R discuss {c |}
                   politics w {c |}      Freq.     Percent        Cum.
{hline 30}{c +}{hline 35}
0. NA. INAP, 5, 8, 9, 0 in Z3 {c |}{res}        946       52.35       52.35
{txt}                     1. OFTEN {c |}{res}        186       10.29       62.65
{txt}                 3. SOMETIMES {c |}{res}        483       26.73       89.37
{txt}                    5. RARELY {c |}{res}        189       10.46       99.83
{txt}                     7. NEVER {c |}{res}          2        0.11       99.94
{txt}                        9. RF {c |}{res}          1        0.06      100.00
{txt}{hline 30}{c +}{hline 35}
                        Total {c |}{res}      1,807      100.00

{txt}Z18. How often does R discuss {c |}
                   politics w {c |}      Freq.     Percent        Cum.
{hline 30}{c +}{hline 35}
0. NA. INAP, 5, 8, 9, 0 in Z5 {c |}{res}      1,257       69.56       69.56
{txt}                     1. OFTEN {c |}{res}        104        5.76       75.32
{txt}                 3. SOMETIMES {c |}{res}        295       16.33       91.64
{txt}                5. RARELY, OR {c |}{res}        147        8.14       99.78
{txt}                     7. NEVER {c |}{res}          4        0.22      100.00
{txt}{hline 30}{c +}{hline 35}
                        Total {c |}{res}      1,807      100.00

{txt}Z22. How often does R discuss {c |}
                   politics w {c |}      Freq.     Percent        Cum.
{hline 30}{c +}{hline 35}
0. NA. INAP, 5, 8, 9, 0 in Z7 {c |}{res}      1,480       81.90       81.90
{txt}                     1. OFTEN {c |}{res}         58        3.21       85.11
{txt}                 3. SOMETIMES {c |}{res}        168        9.30       94.41
{txt}                5. RARELY, OR {c |}{res}         97        5.37       99.78
{txt}                     7. NEVER {c |}{res}          4        0.22      100.00
{txt}{hline 30}{c +}{hline 35}
                        Total {c |}{res}      1,807      100.00
{txt}
{com}.                 *only 4-6 people say 'never' - they are thus lumped in with 'rarely'
.                 recode V001708 (1=3) (3=2) (5=1) (7=1), gen(disc1_freq)
{txt}(1148 differences between V001708 and disc1_freq)

{com}.                 tab V001708 disc1_freq

{txt}Z10. How often does R {c |}   RECODE of V001708 (Z10. How often does R discuss politics w)
   discuss politics w {c |}         0          1          2          3          8          9 {c |}     Total
{hline 22}{c +}{hline 66}{c +}{hline 10}
0. NA. INAP, 5, 8, 9, {c |}{res}       655          0          0          0          0          0 {txt}{c |}{res}       655 
{txt}             1. OFTEN {c |}{res}         0          0          0        354          0          0 {txt}{c |}{res}       354 
{txt}         3. SOMETIMES {c |}{res}         0          0        593          0          0          0 {txt}{c |}{res}       593 
{txt}            5. RARELY {c |}{res}         0        195          0          0          0          0 {txt}{c |}{res}       195 
{txt}             7. NEVER {c |}{res}         0          6          0          0          0          0 {txt}{c |}{res}         6 
{txt}                8. DK {c |}{res}         0          0          0          0          2          0 {txt}{c |}{res}         2 
{txt}                9. RF {c |}{res}         0          0          0          0          0          2 {txt}{c |}{res}         2 
{txt}{hline 22}{c +}{hline 66}{c +}{hline 10}
                Total {c |}{res}       655        201        593        354          2          2 {txt}{c |}{res}     1,807 

{txt}
{com}.                 recode V001716 (1=3) (3=2) (5=1) (7=1), gen(disc2_freq)
{txt}(860 differences between V001716 and disc2_freq)

{com}.                 tab V001716 disc2_freq

                      {txt}{c |}    RECODE of V001716 (Z14. How often does R discuss
Z14. How often does R {c |}                      politics w)
   discuss politics w {c |}         0          1          2          3          9 {c |}     Total
{hline 22}{c +}{hline 55}{c +}{hline 10}
0. NA. INAP, 5, 8, 9, {c |}{res}       946          0          0          0          0 {txt}{c |}{res}       946 
{txt}             1. OFTEN {c |}{res}         0          0          0        186          0 {txt}{c |}{res}       186 
{txt}         3. SOMETIMES {c |}{res}         0          0        483          0          0 {txt}{c |}{res}       483 
{txt}            5. RARELY {c |}{res}         0        189          0          0          0 {txt}{c |}{res}       189 
{txt}             7. NEVER {c |}{res}         0          2          0          0          0 {txt}{c |}{res}         2 
{txt}                9. RF {c |}{res}         0          0          0          0          1 {txt}{c |}{res}         1 
{txt}{hline 22}{c +}{hline 55}{c +}{hline 10}
                Total {c |}{res}       946        191        483        186          1 {txt}{c |}{res}     1,807 

{txt}
{com}.                 
.                 recode V001724 (1=3) (3=2) (5=1) (7=1), gen(disc3_freq)
{txt}(550 differences between V001724 and disc3_freq)

{com}.                 tab V001724 disc3_freq

                      {txt}{c |}  RECODE of V001724 (Z18. How often does R
Z18. How often does R {c |}             discuss politics w)
   discuss politics w {c |}         0          1          2          3 {c |}     Total
{hline 22}{c +}{hline 44}{c +}{hline 10}
0. NA. INAP, 5, 8, 9, {c |}{res}     1,257          0          0          0 {txt}{c |}{res}     1,257 
{txt}             1. OFTEN {c |}{res}         0          0          0        104 {txt}{c |}{res}       104 
{txt}         3. SOMETIMES {c |}{res}         0          0        295          0 {txt}{c |}{res}       295 
{txt}        5. RARELY, OR {c |}{res}         0        147          0          0 {txt}{c |}{res}       147 
{txt}             7. NEVER {c |}{res}         0          4          0          0 {txt}{c |}{res}         4 
{txt}{hline 22}{c +}{hline 44}{c +}{hline 10}
                Total {c |}{res}     1,257        151        295        104 {txt}{c |}{res}     1,807 

{txt}
{com}.                 
.                 recode V001732 (1=3) (3=2) (5=1) (7=1), gen(disc4_freq)
{txt}(327 differences between V001732 and disc4_freq)

{com}.                 tab V001732 disc4_freq

                      {txt}{c |}  RECODE of V001732 (Z22. How often does R
Z22. How often does R {c |}             discuss politics w)
   discuss politics w {c |}         0          1          2          3 {c |}     Total
{hline 22}{c +}{hline 44}{c +}{hline 10}
0. NA. INAP, 5, 8, 9, {c |}{res}     1,480          0          0          0 {txt}{c |}{res}     1,480 
{txt}             1. OFTEN {c |}{res}         0          0          0         58 {txt}{c |}{res}        58 
{txt}         3. SOMETIMES {c |}{res}         0          0        168          0 {txt}{c |}{res}       168 
{txt}        5. RARELY, OR {c |}{res}         0         97          0          0 {txt}{c |}{res}        97 
{txt}             7. NEVER {c |}{res}         0          4          0          0 {txt}{c |}{res}         4 
{txt}{hline 22}{c +}{hline 44}{c +}{hline 10}
                Total {c |}{res}     1,480        101        168         58 {txt}{c |}{res}     1,807 

{txt}
{com}.                 
.                 mvdecode disc1_freq disc2_freq disc3_freq disc4_freq, mv(0 = .a \ 8 = .b \ 9 = .c)
  {txt}disc1_freq:{res}{col 15}659{txt} missing values generated
  disc2_freq:{res}{col 15}947{txt} missing values generated
  disc3_freq:{res}{col 15}1257{txt} missing values generated
  disc4_freq:{res}{col 15}1480{txt} missing values generated

{com}.                 
.                 label def freq 3 "Often" 2 "Sometimes" 1 "Rarely/Never"
{txt}
{com}.                         
.                 foreach var  in disc1_freq disc2_freq disc3_freq disc4_freq {c -(}
{txt}  2{com}.                         label values `var' freq
{txt}  3{com}.                         tab `var'
{txt}  4{com}.                         {c )-}

   {txt}RECODE of {c |}
     V001708 {c |}
   (Z10. How {c |}
often does R {c |}
     discuss {c |}
 politics w) {c |}      Freq.     Percent        Cum.
{hline 13}{c +}{hline 35}
Rarely/Never {c |}{res}        201       17.51       17.51
{txt}   Sometimes {c |}{res}        593       51.66       69.16
{txt}       Often {c |}{res}        354       30.84      100.00
{txt}{hline 13}{c +}{hline 35}
       Total {c |}{res}      1,148      100.00

   {txt}RECODE of {c |}
     V001716 {c |}
   (Z14. How {c |}
often does R {c |}
     discuss {c |}
 politics w) {c |}      Freq.     Percent        Cum.
{hline 13}{c +}{hline 35}
Rarely/Never {c |}{res}        191       22.21       22.21
{txt}   Sometimes {c |}{res}        483       56.16       78.37
{txt}       Often {c |}{res}        186       21.63      100.00
{txt}{hline 13}{c +}{hline 35}
       Total {c |}{res}        860      100.00

   {txt}RECODE of {c |}
     V001724 {c |}
   (Z18. How {c |}
often does R {c |}
     discuss {c |}
 politics w) {c |}      Freq.     Percent        Cum.
{hline 13}{c +}{hline 35}
Rarely/Never {c |}{res}        151       27.45       27.45
{txt}   Sometimes {c |}{res}        295       53.64       81.09
{txt}       Often {c |}{res}        104       18.91      100.00
{txt}{hline 13}{c +}{hline 35}
       Total {c |}{res}        550      100.00

   {txt}RECODE of {c |}
     V001732 {c |}
   (Z22. How {c |}
often does R {c |}
     discuss {c |}
 politics w) {c |}      Freq.     Percent        Cum.
{hline 13}{c +}{hline 35}
Rarely/Never {c |}{res}        101       30.89       30.89
{txt}   Sometimes {c |}{res}        168       51.38       82.26
{txt}       Often {c |}{res}         58       17.74      100.00
{txt}{hline 13}{c +}{hline 35}
       Total {c |}{res}        327      100.00
{txt}
{com}.                         
.                 egen disc_freq = rowmean(disc1_freq disc2_freq disc3_freq disc4_freq)
{txt}(659 missing values generated)

{com}.                 label var disc_freq "Avg. Freq of Political Discussion"
{txt}
{com}.                 
.                 summ disc_freq, detail

              {txt}Avg. Freq of Political Discussion
{hline 61}
      Percentiles      Smallest
 1%    {res}        1              1
{txt} 5%    {res}        1              1
{txt}10%    {res}        1              1       {txt}Obs         {res}       1148
{txt}25%    {res} 1.666667              1       {txt}Sum of Wgt. {res}       1148

{txt}50%    {res}        2                      {txt}Mean          {res} 2.021632
                        {txt}Largest       Std. Dev.     {res} .5758626
{txt}75%    {res}      2.5              3
{txt}90%    {res}        3              3       {txt}Variance      {res} .3316178
{txt}95%    {res}        3              3       {txt}Skewness      {res}-.0695684
{txt}99%    {res}        3              3       {txt}Kurtosis      {res} 2.444665
{txt}
{com}.                 
.                 *Weighted Scale of Exposure to Disagreement
.                 
.                         *Agree/Weight
.                         gen a1f = d1_votea * disc1_freq 
{txt}(985 missing values generated)

{com}.                         gen a2f = d2_votea * disc2_freq 
{txt}(1189 missing values generated)

{com}.                         gen a3f = d3_votea * disc3_freq 
{txt}(1407 missing values generated)

{com}.                         gen a4f = d4_votea * disc4_freq 
{txt}(1574 missing values generated)

{com}.                         egen agree_freq = rowtotal(a1f a2f a3f a4f), missing
{txt}(917 missing values generated)

{com}.                 
.                         *Disagree/Weight
.                         gen d1f = d1_voted * disc1_freq 
{txt}(985 missing values generated)

{com}.                         gen d2f = d2_voted * disc2_freq 
{txt}(1189 missing values generated)

{com}.                         gen d3f = d3_voted * disc3_freq 
{txt}(1407 missing values generated)

{com}.                         gen d4f = d4_voted * disc4_freq 
{txt}(1574 missing values generated)

{com}.                         egen dagree_freq = rowtotal(d1f d2f d3f d4f), missing
{txt}(917 missing values generated)

{com}.         
.                 *Scale
.                                 gen disagree_total_freq = dagree_freq - agree_freq
{txt}(917 missing values generated)

{com}.                                 gen disagree_avg_freq = disagree_total_freq/(cand_disagree + cand_agree)
{txt}(917 missing values generated)

{com}.                                 label var disagree_total_freq "Network Disagreement (Frequency Weighted)"
{txt}
{com}.                                 label var disagree_avg_freq "Network Disagreement (Frequency Weighted)"
{txt}
{com}.                                 
.                                 foreach var in disagree_total_freq disagree_avg_freq {c -(}
{txt}  2{com}.                                         summ `var'
{txt}  3{com}.                                         gen `var'01 = (`var' - r(min))/(r(max)-r(min))
{txt}  4{com}.                                         {c )-}

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
disagree_t~q {c |}{res}       890   -2.392135    3.617565        -12         10
{txt}(917 missing values generated)

    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
disagree_a~q {c |}{res}       890   -1.022753    1.574888         -3          3
{txt}(917 missing values generated)

{com}.                                 
.                                 label var disagree_total_freq01 "Network Disagreement (Frequency Weighted)"
{txt}
{com}.                                 label var disagree_avg_freq01 "Network Disagreement (Frequency Weighted)"
{txt}
{com}.                                 
.                                 
. *Campaign Interest and Caring About Election
.         
. recode  V000301 (1=3) (3=2) (5=1), gen(camp_int)
{txt}(1807 differences between V000301 and camp_int)

{com}. label var camp_int "Campaign Interest"
{txt}
{com}. 
. recode  V000302 (1=1) (3=0) (8=0), gen(cares)
{txt}(405 differences between V000302 and cares)

{com}. label var cares "Cares about Election"
{txt}
{com}. 
. 
. **Partisan Ambivalence**
. 
. mvdecode V000374 V000375 V000376 V000377 V000378 V000380 V000381 V000382 V000383 V000384 V000386 V000387 V000388 V000389 V000390 ///
>         V000392 V000393 V000394 V000395 V000396, mv(0=.)
     {txt}V000374:{res}{col 15}789{txt} missing values generated
     V000375:{res}{col 15}1184{txt} missing values generated
     V000376:{res}{col 15}1460{txt} missing values generated
     V000377:{res}{col 15}1635{txt} missing values generated
     V000378:{res}{col 15}1730{txt} missing values generated
     V000380:{res}{col 15}1025{txt} missing values generated
     V000381:{res}{col 15}1355{txt} missing values generated
     V000382:{res}{col 15}1583{txt} missing values generated
     V000383:{res}{col 15}1704{txt} missing values generated
     V000384:{res}{col 15}1758{txt} missing values generated
     V000386:{res}{col 15}999{txt} missing values generated
     V000387:{res}{col 15}1311{txt} missing values generated
     V000388:{res}{col 15}1535{txt} missing values generated
     V000389:{res}{col 15}1670{txt} missing values generated
     V000390:{res}{col 15}1748{txt} missing values generated
     V000392:{res}{col 15}892{txt} missing values generated
     V000393:{res}{col 15}1254{txt} missing values generated
     V000394:{res}{col 15}1530{txt} missing values generated
     V000395:{res}{col 15}1669{txt} missing values generated
     V000396:{res}{col 15}1744{txt} missing values generated

{com}. 
. 
. 
. gen dem_likes = . 
{txt}(1807 missing values generated)

{com}. replace dem_likes = 0 if V000373 == 5
{txt}(766 real changes made)

{com}. replace dem_likes = 0 if V000373 == 8
{txt}(20 real changes made)

{com}. replace dem_likes = 0 if V000373 == 9
{txt}(3 real changes made)

{com}. replace dem_likes = 1 if V000373 == 1 & V000374 !=. & V000375 == .
{txt}(395 real changes made)

{com}. replace dem_likes = 2 if V000373 == 1 & V000374 !=. & V000375 !=. & V000376 ==. 
{txt}(276 real changes made)

{com}. replace dem_likes = 3 if V000373 == 1 & V000374 !=. & V000375 !=. & V000376 !=. & V000377 ==. 
{txt}(175 real changes made)

{com}. replace dem_likes = 4 if V000373 == 1 & V000374 !=. & V000375 !=. & V000376 !=. & V000377 !=. & V000378 ==.
{txt}(95 real changes made)

{com}. replace dem_likes = 5 if V000373 == 1 &  V000374 !=. & V000375 !=. & V000376 !=. & V000377 !=.  & V000378 !=.
{txt}(77 real changes made)

{com}. 
. 
. 
. gen dem_dislikes = .
{txt}(1807 missing values generated)

{com}. replace dem_dislikes = 0 if V000379 == 5
{txt}(996 real changes made)

{com}. replace dem_dislikes = 0 if V000379 == 8
{txt}(25 real changes made)

{com}. replace dem_dislikes = 0 if V000379 == 9
{txt}(4 real changes made)

{com}. replace dem_dislikes = 1 if V000379 == 1 & V000380 !=. & V000381 == .
{txt}(330 real changes made)

{com}. replace dem_dislikes = 2 if V000379 == 1 & V000380 !=. & V000381 !=. & V000382 ==. 
{txt}(228 real changes made)

{com}. replace dem_dislikes = 3 if V000379 == 1 & V000380 !=. & V000381 !=. & V000382 !=. & V000383 ==. 
{txt}(121 real changes made)

{com}. replace dem_dislikes = 4 if V000379 == 1 & V000380 !=. & V000381 !=. & V000382 !=. & V000383 !=. & V000384 ==.
{txt}(54 real changes made)

{com}. replace dem_dislikes = 5 if V000379 == 1 &  V000380 !=. & V000381 !=. & V000382 !=. & V000383 !=.  & V000384 !=.
{txt}(49 real changes made)

{com}. 
. 
. 
. gen rep_likes = .
{txt}(1807 missing values generated)

{com}. replace rep_likes = 0 if V000385 == 5
{txt}(975 real changes made)

{com}. replace rep_likes = 0 if V000385 == 8
{txt}(20 real changes made)

{com}. replace rep_likes = 0 if V000385 == 9
{txt}(4 real changes made)

{com}. replace rep_likes = 1 if V000385 == 1 & V000386 !=. & V000387 == .
{txt}(312 real changes made)

{com}. replace rep_likes = 2 if V000385 == 1 & V000386 !=. & V000387 !=. & V000388 ==. 
{txt}(224 real changes made)

{com}. replace rep_likes = 3 if V000385 == 1 & V000386 !=. & V000387 !=. & V000388 !=. & V000389 ==. 
{txt}(135 real changes made)

{com}. replace rep_likes = 4 if V000385 == 1 & V000386 !=. & V000387 !=. & V000388 !=. & V000389 !=. & V000390 ==.
{txt}(78 real changes made)

{com}. replace rep_likes = 5 if V000385 == 1 &  V000386 !=. & V000387 !=. & V000388 !=. & V000389 !=.  & V000390 !=.
{txt}(59 real changes made)

{com}. 
. 
. gen rep_dislikes = .
{txt}(1807 missing values generated)

{com}. replace rep_dislikes = 0 if V000391 == 5
{txt}(867 real changes made)

{com}. replace rep_dislikes = 0 if V000391 == 8
{txt}(21 real changes made)

{com}. replace rep_dislikes = 0 if V000391 == 9
{txt}(4 real changes made)

{com}. replace rep_dislikes = 1 if V000391 == 1 & V000392 !=. & V000393 == .
{txt}(362 real changes made)

{com}. replace rep_dislikes = 2 if V000391 == 1 & V000392 !=. & V000393 !=. & V000394 ==. 
{txt}(276 real changes made)

{com}. replace rep_dislikes = 3 if V000391 == 1 & V000392 !=. & V000393 !=. & V000394 !=. & V000327 ==. 
{txt}(0 real changes made)

{com}. replace rep_dislikes = 4 if V000391 == 1 & V000392 !=. & V000393 !=. & V000394 !=. & V000327 !=. & V000396 ==.
{txt}(214 real changes made)

{com}. replace rep_dislikes = 5 if V000391 == 1 &  V000392 !=. & V000393 !=. & V000394 !=. & V000327 !=.  & V000396 !=.
{txt}(63 real changes made)

{com}. 
. 
.         *ID Consistent
.                 gen consistent = . 
{txt}(1807 missing values generated)

{com}.                 replace consistent = dem_likes + rep_dislikes if pid_2  == 1
{txt}(889 real changes made)

{com}.                 replace consistent = dem_dislikes + rep_likes if pid_2  == 0
{txt}(681 real changes made)

{com}.                 label var consistent "Partisan Identity Consistent Likes/Dislikes"
{txt}
{com}. 
.         *ID Conflicting
.                 gen conflicting = . 
{txt}(1807 missing values generated)

{com}.                 replace conflicting = dem_dislikes + rep_likes if pid_2  == 1
{txt}(889 real changes made)

{com}.                 replace conflicting = dem_likes + rep_dislikes if pid_2  == 0
{txt}(681 real changes made)

{com}.                 label var conflicting "Partisan Identity Conclifting Likes/Dislikes"
{txt}
{com}. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. 
. **Candidate Ambivalence (pre-election)
. 
. mvdecode V000306 V000307 V000308 V000309 V000310, mv(0=.)
     {txt}V000306:{res}{col 15}895{txt} missing values generated
     V000307:{res}{col 15}1200{txt} missing values generated
     V000308:{res}{col 15}1420{txt} missing values generated
     V000309:{res}{col 15}1588{txt} missing values generated
     V000310:{res}{col 15}1692{txt} missing values generated

{com}. mvdecode V000312 V000313 V000314 V000315 V000316 V000318 V000319 V000320  ///
>         V000321 V000322 V000324 V000325 V000326 V000327 V000328, mv(0=.)
     {txt}V000312:{res}{col 15}947{txt} missing values generated
     V000313:{res}{col 15}1278{txt} missing values generated
     V000314:{res}{col 15}1522{txt} missing values generated
     V000315:{res}{col 15}1669{txt} missing values generated
     V000316:{res}{col 15}1742{txt} missing values generated
     V000318:{res}{col 15}985{txt} missing values generated
     V000319:{res}{col 15}1264{txt} missing values generated
     V000320:{res}{col 15}1503{txt} missing values generated
     V000321:{res}{col 15}1638{txt} missing values generated
     V000322:{res}{col 15}1713{txt} missing values generated
     V000324:{res}{col 15}922{txt} missing values generated
     V000325:{res}{col 15}1255{txt} missing values generated
     V000326:{res}{col 15}1491{txt} missing values generated
     V000327:{res}{col 15}1641{txt} missing values generated
     V000328:{res}{col 15}1719{txt} missing values generated

{com}. 
. gen gore_likes = . 
{txt}(1807 missing values generated)

{com}. replace gore_likes = 0 if V000305 == 5
{txt}(878 real changes made)

{com}. replace gore_likes = 0 if V000305 == 8
{txt}(13 real changes made)

{com}. replace gore_likes = 0 if V000305 == 9
{txt}(4 real changes made)

{com}. replace gore_likes = 1 if V000305 == 1 & V000306 !=. & V000307 == .
{txt}(305 real changes made)

{com}. replace gore_likes = 2 if V000305 == 1 & V000306 !=. & V000307 !=. & V000308 ==. 
{txt}(220 real changes made)

{com}. replace gore_likes = 3 if V000305 == 1 & V000306 !=. & V000307 !=. & V000308 !=. & V000309 ==. 
{txt}(168 real changes made)

{com}. replace gore_likes = 4 if V000305 == 1 & V000306 !=. & V000307 !=. & V000308 !=. & V000309 !=. & V000310 ==.
{txt}(104 real changes made)

{com}. replace gore_likes = 5 if V000305 == 1 &  V000306 !=. & V000307 !=. & V000308 !=. & V000309 !=.  & V000310 !=.
{txt}(115 real changes made)

{com}. 
. 
. 
. gen gore_dislikes = .
{txt}(1807 missing values generated)

{com}. replace gore_dislikes = 0 if V000311 == 5
{txt}(934 real changes made)

{com}. replace gore_dislikes = 0 if V000311 == 8
{txt}(10 real changes made)

{com}. replace gore_dislikes = 0 if V000311 == 9
{txt}(3 real changes made)

{com}. replace gore_dislikes = 1 if V000311 == 1 & V000312 !=. & V000313 == .
{txt}(331 real changes made)

{com}. replace gore_dislikes = 2 if V000311 == 1 & V000312 !=. & V000313 !=. & V000314 ==. 
{txt}(244 real changes made)

{com}. replace gore_dislikes = 3 if V000311 == 1 & V000312 !=. & V000313 !=. & V000314 !=. & V000315 ==. 
{txt}(147 real changes made)

{com}. replace gore_dislikes = 4 if V000311 == 1 & V000312 !=. & V000313 !=. & V000314 !=. & V000315 !=. & V000316 ==.
{txt}(73 real changes made)

{com}. replace gore_dislikes = 5 if V000311 == 1 &  V000312 !=. & V000313 !=. & V000314 !=. & V000315 !=.  & V000316 !=.
{txt}(65 real changes made)

{com}. 
. 
. 
. gen bush_likes = .
{txt}(1807 missing values generated)

{com}. replace bush_likes = 0 if V000317 == 5
{txt}(970 real changes made)

{com}. replace bush_likes = 0 if V000317 == 8
{txt}(13 real changes made)

{com}. replace bush_likes = 0 if V000317 == 9
{txt}(2 real changes made)

{com}. replace bush_likes = 1 if V000317 == 1 & V000318 !=. & V000319 == .
{txt}(279 real changes made)

{com}. replace bush_likes = 2 if V000317 == 1 & V000318 !=. & V000319 !=. & V000320 ==. 
{txt}(239 real changes made)

{com}. replace bush_likes = 3 if V000317 == 1 & V000318 !=. & V000319 !=. & V000320 !=. & V000321 ==. 
{txt}(135 real changes made)

{com}. replace bush_likes = 4 if V000317 == 1 & V000318 !=. & V000319 !=. & V000320 !=. & V000321 !=. & V000322 ==.
{txt}(75 real changes made)

{com}. replace bush_likes = 5 if V000317 == 1 &  V000318 !=. & V000319 !=. & V000320 !=. & V000321 !=.  & V000322 !=.
{txt}(94 real changes made)

{com}. 
. 
. gen bush_dislikes = .
{txt}(1807 missing values generated)

{com}. replace bush_dislikes = 0 if V000323 == 5
{txt}(899 real changes made)

{com}. replace bush_dislikes = 0 if V000323 == 8
{txt}(20 real changes made)

{com}. replace bush_dislikes = 0 if V000323 == 9
{txt}(3 real changes made)

{com}. replace bush_dislikes = 1 if V000323 == 1 & V000324 !=. & V000325 == .
{txt}(333 real changes made)

{com}. replace bush_dislikes = 2 if V000323 == 1 & V000324 !=. & V000325 !=. & V000326 ==. 
{txt}(236 real changes made)

{com}. replace bush_dislikes = 3 if V000323 == 1 & V000324 !=. & V000325 !=. & V000326 !=. & V000327 ==. 
{txt}(150 real changes made)

{com}. replace bush_dislikes = 4 if V000323 == 1 & V000324 !=. & V000325 !=. & V000326 !=. & V000327 !=. & V000328 ==.
{txt}(78 real changes made)

{com}. replace bush_dislikes = 5 if V000323 == 1 &  V000324 !=. & V000325 !=. & V000326 !=. & V000327 !=.  & V000328 !=.
{txt}(88 real changes made)

{com}. 
. 
.         
.                         
. **Variables for Matching
. recode race (1=0) (2=1) (3=0), gen(black)
{txt}(1788 differences between race and black)

{com}. tabulate pid_str, gen(pstr_)

   {txt}PID Str. {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
     Leaner {c |}{res}        499       31.78       31.78
{txt}       Weak {c |}{res}        489       31.15       62.93
{txt}     Strong {c |}{res}        582       37.07      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,570      100.00
{txt}
{com}. tabulate camp_int, gen(cint_)

   {txt}Campaign {c |}
   Interest {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}        396       21.91       21.91
{txt}          2 {c |}{res}        886       49.03       70.95
{txt}          3 {c |}{res}        525       29.05      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,807      100.00
{txt}
{com}. 
. 
. /*********Cognitive Style*/
. 
. recode V000862 (0=.) (8=.) (1=4) (2=3) (3=2) (4=1) (9=.), gen(opinionated)
{txt}(1807 differences between V000862 and opinionated)

{com}. recode V000866 (0 8 9 = .) , gen(opinion_degree)
{txt}(36 differences between V000866 and opinion_degree)

{com}. 
. foreach var in opinionated opinion_degree {c -(}
{txt}  2{com}.         summ `var'
{txt}  3{com}.         gen `var'01 = (`var' - r(min))/(r(max)-r(min))
{txt}  4{com}.         {c )-}

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 1}opinionated {c |}{res}      1798    2.670189      .86399          1          4
{txt}(9 missing values generated)

    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
opinion_de~e {c |}{res}      1771    3.288538    .9488492          1          5
{txt}(36 missing values generated)

{com}. 
. egen evaluate1 = rowtotal(opinionated01 opinion_degree01), missing
{txt}(7 missing values generated)

{com}. 
.         
. recode V000871 (0 8 9 =.) (1=0) (5=1) , gen(complex)
{txt}(1807 differences between V000871 and complex)

{com}. recode V000870 (0 8 = .) (5=0) (4=0.25) (3=0.50) (2=0.75) (1=1), gen(thinking)
{txt}(1100 differences between V000870 and thinking)

{com}. 
. egen nfc1 = rowtotal(complex thinking), missing
{txt}(5 missing values generated)

{com}.         
. 
{txt}end of do-file

{com}. 
. /***Knowledge***/
. eststo clear
{txt}
{com}. foreach var in disagree_total disagree_avg  disagree_total_freq disagree_total_knowl {c -(}
{txt}  2{com}. eststo: logit def_knowl i.partisan##c.`var' names1 disc_knowl interest i.gender i.race ///
>         i.marital age educ income nfc1 evaluate1 [pweight = V000002a]
{txt}  3{com}.         {c )-}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-185.21815}  
Iteration 1:{space 3}log pseudolikelihood = {res:-152.20928}  
Iteration 2:{space 3}log pseudolikelihood = {res:-149.76191}  
Iteration 3:{space 3}log pseudolikelihood = {res:-149.74767}  
Iteration 4:{space 3}log pseudolikelihood = {res:-149.74766}  
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}       354
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     51.23
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-149.74766{txt}{col 51}Pseudo R2{col 67}= {res}    0.1915

{txt}{hline 26}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 27}{c |}{col 39}    Robust
{col 1}                def_knowl{col 27}{c |}      Coef.{col 39}   Std. Err.{col 51}      z{col 59}   P>|z|{col 67}     [95% Con{col 80}f. Interval]
{hline 26}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}partisan {c |}
{space 13}In-Partisan  {c |}{col 27}{res}{space 2} 1.526562{col 39}{space 2} .4172933{col 50}{space 1}    3.66{col 59}{space 3}0.000{col 67}{space 4} .7086822{col 80}{space 3} 2.344442
{txt}{space 11}disagree_total {c |}{col 27}{res}{space 2} .1843461{col 39}{space 2} .1422974{col 50}{space 1}    1.30{col 59}{space 3}0.195{col 67}{space 4}-.0945517{col 80}{space 3} .4632438
{txt}{space 25} {c |}
partisan#c.disagree_total {c |}
{space 13}In-Partisan  {c |}{col 27}{res}{space 2} .0847402{col 39}{space 2} .2039209{col 50}{space 1}    0.42{col 59}{space 3}0.678{col 67}{space 4}-.3149374{col 80}{space 3} .4844178
{txt}{space 25} {c |}
{space 19}names1 {c |}{col 27}{res}{space 2}-.0342444{col 39}{space 2} .1474544{col 50}{space 1}   -0.23{col 59}{space 3}0.816{col 67}{space 4}-.3232497{col 80}{space 3} .2547609
{txt}{space 15}disc_knowl {c |}{col 27}{res}{space 2}-.8825592{col 39}{space 2} .4002266{col 50}{space 1}   -2.21{col 59}{space 3}0.027{col 67}{space 4}-1.666989{col 80}{space 3}-.0981295
{txt}{space 17}interest {c |}{col 27}{res}{space 2} .4083001{col 39}{space 2}  .228466{col 50}{space 1}    1.79{col 59}{space 3}0.074{col 67}{space 4}-.0394851{col 80}{space 3} .8560852
{txt}{space 25} {c |}
{space 19}gender {c |}
{space 18}Female  {c |}{col 27}{res}{space 2} .2670814{col 39}{space 2} .3316514{col 50}{space 1}    0.81{col 59}{space 3}0.421{col 67}{space 4}-.3829434{col 80}{space 3} .9171062
{txt}{space 25} {c |}
{space 21}race {c |}
{space 19}Black  {c |}{col 27}{res}{space 2} -.667445{col 39}{space 2} .6470999{col 50}{space 1}   -1.03{col 59}{space 3}0.302{col 67}{space 4}-1.935737{col 80}{space 3} .6008475
{txt}{space 19}Other  {c |}{col 27}{res}{space 2}-1.020683{col 39}{space 2}  .473092{col 50}{space 1}   -2.16{col 59}{space 3}0.031{col 67}{space 4}-1.947926{col 80}{space 3}-.0934394
{txt}{space 25} {c |}
{space 18}marital {c |}
{space 17}Married  {c |}{col 27}{res}{space 2}-.4014397{col 39}{space 2}   .36276{col 50}{space 1}   -1.11{col 59}{space 3}0.268{col 67}{space 4}-1.112436{col 80}{space 3} .3095569
{txt}{space 22}age {c |}{col 27}{res}{space 2}-.0069177{col 39}{space 2} .0104119{col 50}{space 1}   -0.66{col 59}{space 3}0.506{col 67}{space 4}-.0273247{col 80}{space 3} .0134893
{txt}{space 21}educ {c |}{col 27}{res}{space 2} .4133497{col 39}{space 2} .2003964{col 50}{space 1}    2.06{col 59}{space 3}0.039{col 67}{space 4}   .02058{col 80}{space 3} .8061194
{txt}{space 19}income {c |}{col 27}{res}{space 2} .1823773{col 39}{space 2} .0613934{col 50}{space 1}    2.97{col 59}{space 3}0.003{col 67}{space 4} .0620485{col 80}{space 3} .3027061
{txt}{space 21}nfc1 {c |}{col 27}{res}{space 2} .6941822{col 39}{space 2}  .257379{col 50}{space 1}    2.70{col 59}{space 3}0.007{col 67}{space 4} .1897287{col 80}{space 3} 1.198636
{txt}{space 16}evaluate1 {c |}{col 27}{res}{space 2} -.307319{col 39}{space 2} .3806711{col 50}{space 1}   -0.81{col 59}{space 3}0.419{col 67}{space 4}-1.053421{col 80}{space 3} .4387826
{txt}{space 20}_cons {c |}{col 27}{res}{space 2}-.8843644{col 39}{space 2} 1.180354{col 50}{space 1}   -0.75{col 59}{space 3}0.454{col 67}{space 4}-3.197816{col 80}{space 3} 1.429087
{txt}{hline 26}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-185.21815}  
Iteration 1:{space 3}log pseudolikelihood = {res:-152.95005}  
Iteration 2:{space 3}log pseudolikelihood = {res:-150.71985}  
Iteration 3:{space 3}log pseudolikelihood = {res:-150.70833}  
Iteration 4:{space 3}log pseudolikelihood = {res:-150.70833}  
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}       354
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     50.27
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-150.70833{txt}{col 51}Pseudo R2{col 67}= {res}    0.1863

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}              def_knowl{col 25}{c |}      Coef.{col 37}   Std. Err.{col 49}      z{col 57}   P>|z|{col 65}     [95% Con{col 78}f. Interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}partisan {c |}
{space 11}In-Partisan  {c |}{col 25}{res}{space 2} 1.291784{col 37}{space 2} .3836507{col 48}{space 1}    3.37{col 57}{space 3}0.001{col 65}{space 4} .5398423{col 78}{space 3} 2.043726
{txt}{space 11}disagree_avg {c |}{col 25}{res}{space 2} .5288355{col 37}{space 2} .3046562{col 48}{space 1}    1.74{col 57}{space 3}0.083{col 65}{space 4}-.0682796{col 78}{space 3} 1.125951
{txt}{space 23} {c |}
partisan#c.disagree_avg {c |}
{space 11}In-Partisan  {c |}{col 25}{res}{space 2}-.2133626{col 37}{space 2} .4482848{col 48}{space 1}   -0.48{col 57}{space 3}0.634{col 65}{space 4}-1.091985{col 78}{space 3} .6652594
{txt}{space 23} {c |}
{space 17}names1 {c |}{col 25}{res}{space 2}-.1295451{col 37}{space 2} .1407083{col 48}{space 1}   -0.92{col 57}{space 3}0.357{col 65}{space 4}-.4053282{col 78}{space 3}  .146238
{txt}{space 13}disc_knowl {c |}{col 25}{res}{space 2}-.9253209{col 37}{space 2} .4051917{col 48}{space 1}   -2.28{col 57}{space 3}0.022{col 65}{space 4}-1.719482{col 78}{space 3}-.1311597
{txt}{space 15}interest {c |}{col 25}{res}{space 2} .3999015{col 37}{space 2} .2276467{col 48}{space 1}    1.76{col 57}{space 3}0.079{col 65}{space 4}-.0462779{col 78}{space 3} .8460809
{txt}{space 23} {c |}
{space 17}gender {c |}
{space 16}Female  {c |}{col 25}{res}{space 2} .2429156{col 37}{space 2} .3391688{col 48}{space 1}    0.72{col 57}{space 3}0.474{col 65}{space 4}-.4218429{col 78}{space 3} .9076742
{txt}{space 23} {c |}
{space 19}race {c |}
{space 17}Black  {c |}{col 25}{res}{space 2}-.6615158{col 37}{space 2} .6486464{col 48}{space 1}   -1.02{col 57}{space 3}0.308{col 65}{space 4}-1.932839{col 78}{space 3} .6098078
{txt}{space 17}Other  {c |}{col 25}{res}{space 2}-1.037548{col 37}{space 2} .4796357{col 48}{space 1}   -2.16{col 57}{space 3}0.031{col 65}{space 4}-1.977616{col 78}{space 3}-.0974788
{txt}{space 23} {c |}
{space 16}marital {c |}
{space 15}Married  {c |}{col 25}{res}{space 2} -.405215{col 37}{space 2} .3609237{col 48}{space 1}   -1.12{col 57}{space 3}0.262{col 65}{space 4}-1.112613{col 78}{space 3} .3021826
{txt}{space 20}age {c |}{col 25}{res}{space 2}-.0068941{col 37}{space 2} .0104856{col 48}{space 1}   -0.66{col 57}{space 3}0.511{col 65}{space 4}-.0274455{col 78}{space 3} .0136574
{txt}{space 19}educ {c |}{col 25}{res}{space 2} .3835191{col 37}{space 2} .1977496{col 48}{space 1}    1.94{col 57}{space 3}0.052{col 65}{space 4}-.0040629{col 78}{space 3} .7711011
{txt}{space 17}income {c |}{col 25}{res}{space 2} .1831563{col 37}{space 2} .0615196{col 48}{space 1}    2.98{col 57}{space 3}0.003{col 65}{space 4} .0625801{col 78}{space 3} .3037325
{txt}{space 19}nfc1 {c |}{col 25}{res}{space 2} .7141468{col 37}{space 2} .2607385{col 48}{space 1}    2.74{col 57}{space 3}0.006{col 65}{space 4} .2031088{col 78}{space 3} 1.225185
{txt}{space 14}evaluate1 {c |}{col 25}{res}{space 2}-.2935257{col 37}{space 2} .3761722{col 48}{space 1}   -0.78{col 57}{space 3}0.435{col 65}{space 4} -1.03081{col 78}{space 3} .4437583
{txt}{space 18}_cons {c |}{col 25}{res}{space 2}-.3921577{col 37}{space 2} 1.157367{col 48}{space 1}   -0.34{col 57}{space 3}0.735{col 65}{space 4}-2.660555{col 78}{space 3} 1.876239
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-185.21815}  
Iteration 1:{space 3}log pseudolikelihood = {res:-152.82991}  
Iteration 2:{space 3}log pseudolikelihood = {res:-150.48472}  
Iteration 3:{space 3}log pseudolikelihood = {res:-150.47227}  
Iteration 4:{space 3}log pseudolikelihood = {res:-150.47227}  
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}       354
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     50.74
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-150.47227{txt}{col 51}Pseudo R2{col 67}= {res}    0.1876

{txt}{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}{col 44}    Robust
{col 1}                     def_knowl{col 32}{c |}      Coef.{col 44}   Std. Err.{col 56}      z{col 64}   P>|z|{col 72}     [95% Con{col 85}f. Interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}partisan {c |}
{space 18}In-Partisan  {c |}{col 32}{res}{space 2}  1.51301{col 44}{space 2} .4129354{col 55}{space 1}    3.66{col 64}{space 3}0.000{col 72}{space 4}  .703671{col 85}{space 3} 2.322348
{txt}{space 11}disagree_total_freq {c |}{col 32}{res}{space 2} .0752838{col 44}{space 2} .0636238{col 55}{space 1}    1.18{col 64}{space 3}0.237{col 72}{space 4}-.0494165{col 85}{space 3} .1999841
{txt}{space 30} {c |}
partisan#c.disagree_total_freq {c |}
{space 18}In-Partisan  {c |}{col 32}{res}{space 2} .0294738{col 44}{space 2} .0860826{col 55}{space 1}    0.34{col 64}{space 3}0.732{col 72}{space 4} -.139245{col 85}{space 3} .1981925
{txt}{space 30} {c |}
{space 24}names1 {c |}{col 32}{res}{space 2}-.0348382{col 44}{space 2} .1507235{col 55}{space 1}   -0.23{col 64}{space 3}0.817{col 72}{space 4}-.3302509{col 85}{space 3} .2605745
{txt}{space 20}disc_knowl {c |}{col 32}{res}{space 2} -.831261{col 44}{space 2} .4038049{col 55}{space 1}   -2.06{col 64}{space 3}0.040{col 72}{space 4}-1.622704{col 85}{space 3} -.039818
{txt}{space 22}interest {c |}{col 32}{res}{space 2} .4067979{col 44}{space 2} .2312021{col 55}{space 1}    1.76{col 64}{space 3}0.078{col 72}{space 4}-.0463499{col 85}{space 3} .8599457
{txt}{space 30} {c |}
{space 24}gender {c |}
{space 23}Female  {c |}{col 32}{res}{space 2} .2679368{col 44}{space 2} .3338513{col 55}{space 1}    0.80{col 64}{space 3}0.422{col 72}{space 4}-.3863997{col 85}{space 3} .9222732
{txt}{space 30} {c |}
{space 26}race {c |}
{space 24}Black  {c |}{col 32}{res}{space 2}-.6700192{col 44}{space 2} .6471722{col 55}{space 1}   -1.04{col 64}{space 3}0.301{col 72}{space 4}-1.938454{col 85}{space 3}  .598415
{txt}{space 24}Other  {c |}{col 32}{res}{space 2}-1.042034{col 44}{space 2} .4772767{col 55}{space 1}   -2.18{col 64}{space 3}0.029{col 72}{space 4}-1.977479{col 85}{space 3}-.1065889
{txt}{space 30} {c |}
{space 23}marital {c |}
{space 22}Married  {c |}{col 32}{res}{space 2}-.4041532{col 44}{space 2} .3598243{col 55}{space 1}   -1.12{col 64}{space 3}0.261{col 72}{space 4}-1.109396{col 85}{space 3} .3010895
{txt}{space 27}age {c |}{col 32}{res}{space 2}-.0061227{col 44}{space 2} .0106269{col 55}{space 1}   -0.58{col 64}{space 3}0.565{col 72}{space 4} -.026951{col 85}{space 3} .0147056
{txt}{space 26}educ {c |}{col 32}{res}{space 2} .4035512{col 44}{space 2} .2002503{col 55}{space 1}    2.02{col 64}{space 3}0.044{col 72}{space 4} .0110678{col 85}{space 3} .7960347
{txt}{space 24}income {c |}{col 32}{res}{space 2} .1806206{col 44}{space 2} .0614028{col 55}{space 1}    2.94{col 64}{space 3}0.003{col 72}{space 4} .0602734{col 85}{space 3} .3009678
{txt}{space 26}nfc1 {c |}{col 32}{res}{space 2} .7027584{col 44}{space 2} .2577483{col 55}{space 1}    2.73{col 64}{space 3}0.006{col 72}{space 4} .1975811{col 85}{space 3} 1.207936
{txt}{space 21}evaluate1 {c |}{col 32}{res}{space 2}-.3083088{col 44}{space 2} .3808163{col 55}{space 1}   -0.81{col 64}{space 3}0.418{col 72}{space 4}-1.054695{col 85}{space 3} .4380773
{txt}{space 25}_cons {c |}{col 32}{res}{space 2}-1.022739{col 44}{space 2} 1.200458{col 55}{space 1}   -0.85{col 64}{space 3}0.394{col 72}{space 4}-3.375595{col 85}{space 3} 1.330116
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-185.21815}  
Iteration 1:{space 3}log pseudolikelihood = {res:-151.70069}  
Iteration 2:{space 3}log pseudolikelihood = {res:-149.20551}  
Iteration 3:{space 3}log pseudolikelihood = {res:-149.19138}  
Iteration 4:{space 3}log pseudolikelihood = {res:-149.19138}  
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}       354
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     51.41
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-149.19138{txt}{col 51}Pseudo R2{col 67}= {res}    0.1945

{txt}{hline 32}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 33}{c |}{col 45}    Robust
{col 1}                      def_knowl{col 33}{c |}      Coef.{col 45}   Std. Err.{col 57}      z{col 65}   P>|z|{col 73}     [95% Con{col 86}f. Interval]
{hline 32}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 23}partisan {c |}
{space 19}In-Partisan  {c |}{col 33}{res}{space 2}  1.47212{col 45}{space 2} .4166899{col 56}{space 1}    3.53{col 65}{space 3}0.000{col 73}{space 4}  .655423{col 86}{space 3} 2.288817
{txt}{space 11}disagree_total_knowl {c |}{col 33}{res}{space 2} .0944301{col 45}{space 2}  .058512{col 56}{space 1}    1.61{col 65}{space 3}0.107{col 73}{space 4}-.0202513{col 86}{space 3} .2091114
{txt}{space 31} {c |}
partisan#c.disagree_total_knowl {c |}
{space 19}In-Partisan  {c |}{col 33}{res}{space 2} .0211821{col 45}{space 2} .0796216{col 56}{space 1}    0.27{col 65}{space 3}0.790{col 73}{space 4}-.1348735{col 86}{space 3} .1772376
{txt}{space 31} {c |}
{space 25}names1 {c |}{col 33}{res}{space 2}-.0206091{col 45}{space 2} .1495228{col 56}{space 1}   -0.14{col 65}{space 3}0.890{col 73}{space 4}-.3136683{col 86}{space 3} .2724502
{txt}{space 21}disc_knowl {c |}{col 33}{res}{space 2}-.7549891{col 45}{space 2} .4088426{col 56}{space 1}   -1.85{col 65}{space 3}0.065{col 73}{space 4}-1.556306{col 86}{space 3} .0463277
{txt}{space 23}interest {c |}{col 33}{res}{space 2} .4099514{col 45}{space 2} .2283308{col 56}{space 1}    1.80{col 65}{space 3}0.073{col 73}{space 4}-.0375689{col 86}{space 3} .8574716
{txt}{space 31} {c |}
{space 25}gender {c |}
{space 24}Female  {c |}{col 33}{res}{space 2} .2654205{col 45}{space 2} .3347724{col 56}{space 1}    0.79{col 65}{space 3}0.428{col 73}{space 4}-.3907213{col 86}{space 3} .9215623
{txt}{space 31} {c |}
{space 27}race {c |}
{space 25}Black  {c |}{col 33}{res}{space 2}-.6823564{col 45}{space 2}  .646825{col 56}{space 1}   -1.05{col 65}{space 3}0.291{col 73}{space 4} -1.95011{col 86}{space 3} .5853974
{txt}{space 25}Other  {c |}{col 33}{res}{space 2} -1.01007{col 45}{space 2} .4689789{col 56}{space 1}   -2.15{col 65}{space 3}0.031{col 73}{space 4}-1.929252{col 86}{space 3}-.0908886
{txt}{space 31} {c |}
{space 24}marital {c |}
{space 23}Married  {c |}{col 33}{res}{space 2}-.3900606{col 45}{space 2} .3608531{col 56}{space 1}   -1.08{col 65}{space 3}0.280{col 73}{space 4} -1.09732{col 86}{space 3} .3171984
{txt}{space 28}age {c |}{col 33}{res}{space 2}-.0066939{col 45}{space 2} .0104862{col 56}{space 1}   -0.64{col 65}{space 3}0.523{col 73}{space 4}-.0272465{col 86}{space 3} .0138587
{txt}{space 27}educ {c |}{col 33}{res}{space 2} .4145585{col 45}{space 2}   .19878{col 56}{space 1}    2.09{col 65}{space 3}0.037{col 73}{space 4}  .024957{col 86}{space 3} .8041601
{txt}{space 25}income {c |}{col 33}{res}{space 2} .1798775{col 45}{space 2} .0601797{col 56}{space 1}    2.99{col 65}{space 3}0.003{col 73}{space 4} .0619274{col 86}{space 3} .2978276
{txt}{space 27}nfc1 {c |}{col 33}{res}{space 2} .7064851{col 45}{space 2} .2559133{col 56}{space 1}    2.76{col 65}{space 3}0.006{col 73}{space 4} .2049042{col 86}{space 3} 1.208066
{txt}{space 22}evaluate1 {c |}{col 33}{res}{space 2}-.2829141{col 45}{space 2} .3758838{col 56}{space 1}   -0.75{col 65}{space 3}0.452{col 73}{space 4}-1.019633{col 86}{space 3} .4538047
{txt}{space 26}_cons {c |}{col 33}{res}{space 2}-1.200789{col 45}{space 2} 1.212011{col 56}{space 1}   -0.99{col 65}{space 3}0.322{col 73}{space 4}-3.576286{col 86}{space 3} 1.174709
{txt}{hline 32}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{com}.                         
. esttab using 2000_ALTMEASURES_KNOWL.rtf, onecell nobaselevels replace label pr2 aic bic se star(+ 0.10 * 0.05 ** 0.01) ///
>         mtitles("Original Measure" "/(D+A)" "Weight: Disc. Freq" "Weight: Disc Knowl.")  ///
>         title({c -(}\b Table XX.{c )-} "Alternative Measures of Disagreement - 2000 ANES (Knowledge)") ///
>         rename(disagree_total Disagreement disagree_avg Disagreement disagree_total_freq Disagreement ///
>                                 disagree_total_knowl Disagreement) 
{res}{txt}(output written to {browse  `"2000_ALTMEASURES_KNOWL.rtf"'})

{com}.                                 
. eststo clear
{txt}
{com}. 
. 
. /***Economic Evaluations***/
. 
. eststo clear
{txt}
{com}. foreach var in disagree_total disagree_avg  disagree_total_freq disagree_total_knowl {c -(}
{txt}  2{com}.         eststo: ologit econ_post3 i.partisan##c.`var' names1 disc_knowl interest age educ income ///
>         i.gender i.race i.employed i.marital nfc1 evaluate1 [pweight = V000002a]
{txt}  3{com}.         {c )-}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-182.52901}  
Iteration 1:{space 3}log pseudolikelihood = {res:-157.06003}  
Iteration 2:{space 3}log pseudolikelihood = {res:-153.62831}  
Iteration 3:{space 3}log pseudolikelihood = {res:-153.56935}  
Iteration 4:{space 3}log pseudolikelihood = {res:-153.56924}  
Iteration 5:{space 3}log pseudolikelihood = {res:-153.56924}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       369
{txt}{col 51}Wald chi2({res}18{txt}){col 67}= {res}     59.77
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-153.56924{txt}{col 51}Pseudo R2{col 67}= {res}    0.1587

{txt}{hline 34}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 35}{c |}{col 47}    Robust
{col 1}                       econ_post3{col 35}{c |}      Coef.{col 47}   Std. Err.{col 59}      z{col 67}   P>|z|{col 75}     [95% Con{col 88}f. Interval]
{hline 34}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 25}partisan {c |}
{space 21}In-Partisan  {c |}{col 35}{res}{space 2} 2.563918{col 47}{space 2} .4616516{col 58}{space 1}    5.55{col 67}{space 3}0.000{col 75}{space 4} 1.659097{col 88}{space 3} 3.468738
{txt}{space 19}disagree_total {c |}{col 35}{res}{space 2}-.0550876{col 47}{space 2} .1335333{col 58}{space 1}   -0.41{col 67}{space 3}0.680{col 75}{space 4}-.3168081{col 88}{space 3} .2066329
{txt}{space 33} {c |}
{space 8}partisan#c.disagree_total {c |}
{space 21}In-Partisan  {c |}{col 35}{res}{space 2} .3607998{col 47}{space 2} .1915618{col 58}{space 1}    1.88{col 67}{space 3}0.060{col 75}{space 4}-.0146543{col 88}{space 3} .7362539
{txt}{space 33} {c |}
{space 27}names1 {c |}{col 35}{res}{space 2} .1075973{col 47}{space 2} .1635283{col 58}{space 1}    0.66{col 67}{space 3}0.511{col 75}{space 4}-.2129123{col 88}{space 3} .4281069
{txt}{space 23}disc_knowl {c |}{col 35}{res}{space 2}-.2268724{col 47}{space 2}  .440898{col 58}{space 1}   -0.51{col 67}{space 3}0.607{col 75}{space 4}-1.091017{col 88}{space 3} .6372718
{txt}{space 25}interest {c |}{col 35}{res}{space 2} .4399565{col 47}{space 2} .2530909{col 58}{space 1}    1.74{col 67}{space 3}0.082{col 75}{space 4}-.0560925{col 88}{space 3} .9360055
{txt}{space 30}age {c |}{col 35}{res}{space 2} -.004438{col 47}{space 2} .0142175{col 58}{space 1}   -0.31{col 67}{space 3}0.755{col 75}{space 4}-.0323038{col 88}{space 3} .0234278
{txt}{space 29}educ {c |}{col 35}{res}{space 2} .2279011{col 47}{space 2}  .218547{col 58}{space 1}    1.04{col 67}{space 3}0.297{col 75}{space 4}-.2004431{col 88}{space 3} .6562454
{txt}{space 27}income {c |}{col 35}{res}{space 2} .0808797{col 47}{space 2} .0468643{col 58}{space 1}    1.73{col 67}{space 3}0.084{col 75}{space 4}-.0109726{col 88}{space 3}  .172732
{txt}{space 33} {c |}
{space 27}gender {c |}
{space 26}Female  {c |}{col 35}{res}{space 2}-.4066629{col 47}{space 2} .3634013{col 58}{space 1}   -1.12{col 67}{space 3}0.263{col 75}{space 4}-1.118916{col 88}{space 3} .3055905
{txt}{space 33} {c |}
{space 29}race {c |}
{space 27}Black  {c |}{col 35}{res}{space 2}-.8831728{col 47}{space 2} .6481319{col 58}{space 1}   -1.36{col 67}{space 3}0.173{col 75}{space 4}-2.153488{col 88}{space 3} .3871425
{txt}{space 27}Other  {c |}{col 35}{res}{space 2}-.9155465{col 47}{space 2} .5165622{col 58}{space 1}   -1.77{col 67}{space 3}0.076{col 75}{space 4} -1.92799{col 88}{space 3} .0968967
{txt}{space 33} {c |}
{space 25}employed {c |}
{space 22}Unemployed  {c |}{col 35}{res}{space 2}-.3509089{col 47}{space 2} .8435707{col 58}{space 1}   -0.42{col 67}{space 3}0.677{col 75}{space 4}-2.004277{col 88}{space 3} 1.302459
{txt}{space 25}Retired  {c |}{col 35}{res}{space 2} .9212683{col 47}{space 2} .5816034{col 58}{space 1}    1.58{col 67}{space 3}0.113{col 75}{space 4}-.2186533{col 88}{space 3}  2.06119
{txt}Perm. Disabled/Homemaker/Student  {c |}{col 35}{res}{space 2}-.0846457{col 47}{space 2} .4300588{col 58}{space 1}   -0.20{col 67}{space 3}0.844{col 75}{space 4}-.9275454{col 88}{space 3}  .758254
{txt}{space 33} {c |}
{space 26}marital {c |}
{space 25}Married  {c |}{col 35}{res}{space 2} .4300987{col 47}{space 2} .3620485{col 58}{space 1}    1.19{col 67}{space 3}0.235{col 75}{space 4}-.2795033{col 88}{space 3} 1.139701
{txt}{space 29}nfc1 {c |}{col 35}{res}{space 2} .3949015{col 47}{space 2} .3074039{col 58}{space 1}    1.28{col 67}{space 3}0.199{col 75}{space 4} -.207599{col 88}{space 3}  .997402
{txt}{space 24}evaluate1 {c |}{col 35}{res}{space 2}-.7345293{col 47}{space 2} .4077131{col 58}{space 1}   -1.80{col 67}{space 3}0.072{col 75}{space 4}-1.533632{col 88}{space 3} .0645737
{txt}{hline 34}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                            /cut1 {c |}{col 35}{res}{space 2} -1.47553{col 47}{space 2} 1.350835{col 75}{space 4}-4.123119{col 88}{space 3} 1.172058
{txt}                            /cut2 {c |}{col 35}{res}{space 2}  1.04264{col 47}{space 2} 1.324679{col 75}{space 4}-1.553683{col 88}{space 3} 3.638962
{txt}{hline 34}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-182.52901}  
Iteration 1:{space 3}log pseudolikelihood = {res:-156.43785}  
Iteration 2:{space 3}log pseudolikelihood = {res:-152.68781}  
Iteration 3:{space 3}log pseudolikelihood = {res:-152.61757}  
Iteration 4:{space 3}log pseudolikelihood = {res:-152.61755}  
Iteration 5:{space 3}log pseudolikelihood = {res:-152.61755}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       369
{txt}{col 51}Wald chi2({res}18{txt}){col 67}= {res}     59.72
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-152.61755{txt}{col 51}Pseudo R2{col 67}= {res}    0.1639

{txt}{hline 34}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 35}{c |}{col 47}    Robust
{col 1}                       econ_post3{col 35}{c |}      Coef.{col 47}   Std. Err.{col 59}      z{col 67}   P>|z|{col 75}     [95% Con{col 88}f. Interval]
{hline 34}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 25}partisan {c |}
{space 21}In-Partisan  {c |}{col 35}{res}{space 2}  2.68272{col 47}{space 2} .5385844{col 58}{space 1}    4.98{col 67}{space 3}0.000{col 75}{space 4} 1.627114{col 88}{space 3} 3.738326
{txt}{space 21}disagree_avg {c |}{col 35}{res}{space 2}-.0313062{col 47}{space 2} .2663885{col 58}{space 1}   -0.12{col 67}{space 3}0.906{col 75}{space 4}-.5534181{col 88}{space 3} .4908058
{txt}{space 33} {c |}
{space 10}partisan#c.disagree_avg {c |}
{space 21}In-Partisan  {c |}{col 35}{res}{space 2} .9693364{col 47}{space 2} .5661843{col 58}{space 1}    1.71{col 67}{space 3}0.087{col 75}{space 4}-.1403643{col 88}{space 3} 2.079037
{txt}{space 33} {c |}
{space 27}names1 {c |}{col 35}{res}{space 2}  .075702{col 47}{space 2} .1492675{col 58}{space 1}    0.51{col 67}{space 3}0.612{col 75}{space 4}-.2168569{col 88}{space 3} .3682608
{txt}{space 23}disc_knowl {c |}{col 35}{res}{space 2}-.2498598{col 47}{space 2} .4420697{col 58}{space 1}   -0.57{col 67}{space 3}0.572{col 75}{space 4}  -1.1163{col 88}{space 3} .6165809
{txt}{space 25}interest {c |}{col 35}{res}{space 2} .4756484{col 47}{space 2}  .251828{col 58}{space 1}    1.89{col 67}{space 3}0.059{col 75}{space 4}-.0179254{col 88}{space 3} .9692222
{txt}{space 30}age {c |}{col 35}{res}{space 2}-.0021744{col 47}{space 2} .0137188{col 58}{space 1}   -0.16{col 67}{space 3}0.874{col 75}{space 4}-.0290628{col 88}{space 3} .0247139
{txt}{space 29}educ {c |}{col 35}{res}{space 2} .2327711{col 47}{space 2} .2133854{col 58}{space 1}    1.09{col 67}{space 3}0.275{col 75}{space 4}-.1854566{col 88}{space 3} .6509988
{txt}{space 27}income {c |}{col 35}{res}{space 2} .0774897{col 47}{space 2} .0462522{col 58}{space 1}    1.68{col 67}{space 3}0.094{col 75}{space 4}-.0131628{col 88}{space 3} .1681423
{txt}{space 33} {c |}
{space 27}gender {c |}
{space 26}Female  {c |}{col 35}{res}{space 2}-.4038755{col 47}{space 2} .3635128{col 58}{space 1}   -1.11{col 67}{space 3}0.267{col 75}{space 4}-1.116348{col 88}{space 3} .3085965
{txt}{space 33} {c |}
{space 29}race {c |}
{space 27}Black  {c |}{col 35}{res}{space 2}-.8818612{col 47}{space 2}  .638358{col 58}{space 1}   -1.38{col 67}{space 3}0.167{col 75}{space 4} -2.13302{col 88}{space 3} .3692975
{txt}{space 27}Other  {c |}{col 35}{res}{space 2}-.8501289{col 47}{space 2} .5295586{col 58}{space 1}   -1.61{col 67}{space 3}0.108{col 75}{space 4}-1.888045{col 88}{space 3} .1877868
{txt}{space 33} {c |}
{space 25}employed {c |}
{space 22}Unemployed  {c |}{col 35}{res}{space 2}-.3620468{col 47}{space 2} .8746278{col 58}{space 1}   -0.41{col 67}{space 3}0.679{col 75}{space 4}-2.076286{col 88}{space 3} 1.352192
{txt}{space 25}Retired  {c |}{col 35}{res}{space 2} .8424126{col 47}{space 2} .5713063{col 58}{space 1}    1.47{col 67}{space 3}0.140{col 75}{space 4}-.2773271{col 88}{space 3} 1.962152
{txt}Perm. Disabled/Homemaker/Student  {c |}{col 35}{res}{space 2}-.0725267{col 47}{space 2} .4244216{col 58}{space 1}   -0.17{col 67}{space 3}0.864{col 75}{space 4}-.9043777{col 88}{space 3} .7593243
{txt}{space 33} {c |}
{space 26}marital {c |}
{space 25}Married  {c |}{col 35}{res}{space 2} .4240489{col 47}{space 2} .3613944{col 58}{space 1}    1.17{col 67}{space 3}0.241{col 75}{space 4}-.2842712{col 88}{space 3} 1.132369
{txt}{space 29}nfc1 {c |}{col 35}{res}{space 2} .3906504{col 47}{space 2} .3038105{col 58}{space 1}    1.29{col 67}{space 3}0.199{col 75}{space 4}-.2048072{col 88}{space 3}  .986108
{txt}{space 24}evaluate1 {c |}{col 35}{res}{space 2}-.7384399{col 47}{space 2} .4097885{col 58}{space 1}   -1.80{col 67}{space 3}0.072{col 75}{space 4}-1.541611{col 88}{space 3} .0647309
{txt}{hline 34}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                            /cut1 {c |}{col 35}{res}{space 2}-1.487601{col 47}{space 2}  1.31839{col 75}{space 4}-4.071597{col 88}{space 3} 1.096396
{txt}                            /cut2 {c |}{col 35}{res}{space 2} 1.035487{col 47}{space 2} 1.298679{col 75}{space 4}-1.509877{col 88}{space 3} 3.580852
{txt}{hline 34}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-182.52901}  
Iteration 1:{space 3}log pseudolikelihood = {res:-157.03714}  
Iteration 2:{space 3}log pseudolikelihood = {res:-153.62494}  
Iteration 3:{space 3}log pseudolikelihood = {res:-153.57093}  
Iteration 4:{space 3}log pseudolikelihood = {res:-153.57085}  
Iteration 5:{space 3}log pseudolikelihood = {res:-153.57085}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       369
{txt}{col 51}Wald chi2({res}18{txt}){col 67}= {res}     60.78
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-153.57085{txt}{col 51}Pseudo R2{col 67}= {res}    0.1586

{txt}{hline 34}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 35}{c |}{col 47}    Robust
{col 1}                       econ_post3{col 35}{c |}      Coef.{col 47}   Std. Err.{col 59}      z{col 67}   P>|z|{col 75}     [95% Con{col 88}f. Interval]
{hline 34}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 25}partisan {c |}
{space 21}In-Partisan  {c |}{col 35}{res}{space 2} 2.563256{col 47}{space 2} .4484462{col 58}{space 1}    5.72{col 67}{space 3}0.000{col 75}{space 4} 1.684318{col 88}{space 3} 3.442195
{txt}{space 14}disagree_total_freq {c |}{col 35}{res}{space 2}-.0257874{col 47}{space 2} .0621022{col 58}{space 1}   -0.42{col 67}{space 3}0.678{col 75}{space 4}-.1475055{col 88}{space 3} .0959307
{txt}{space 33} {c |}
{space 3}partisan#c.disagree_total_freq {c |}
{space 21}In-Partisan  {c |}{col 35}{res}{space 2} .1586606{col 47}{space 2} .0831796{col 58}{space 1}    1.91{col 67}{space 3}0.056{col 75}{space 4}-.0043685{col 88}{space 3} .3216897
{txt}{space 33} {c |}
{space 27}names1 {c |}{col 35}{res}{space 2} .1107324{col 47}{space 2} .1670021{col 58}{space 1}    0.66{col 67}{space 3}0.507{col 75}{space 4}-.2165856{col 88}{space 3} .4380505
{txt}{space 23}disc_knowl {c |}{col 35}{res}{space 2}-.1936688{col 47}{space 2} .4491711{col 58}{space 1}   -0.43{col 67}{space 3}0.666{col 75}{space 4}-1.074028{col 88}{space 3} .6866904
{txt}{space 25}interest {c |}{col 35}{res}{space 2} .4319193{col 47}{space 2} .2536581{col 58}{space 1}    1.70{col 67}{space 3}0.089{col 75}{space 4}-.0652415{col 88}{space 3}   .92908
{txt}{space 30}age {c |}{col 35}{res}{space 2}-.0031377{col 47}{space 2} .0139159{col 58}{space 1}   -0.23{col 67}{space 3}0.822{col 75}{space 4}-.0304124{col 88}{space 3}  .024137
{txt}{space 29}educ {c |}{col 35}{res}{space 2} .2259221{col 47}{space 2} .2230531{col 58}{space 1}    1.01{col 67}{space 3}0.311{col 75}{space 4}-.2112539{col 88}{space 3} .6630981
{txt}{space 27}income {c |}{col 35}{res}{space 2} .0796423{col 47}{space 2} .0466933{col 58}{space 1}    1.71{col 67}{space 3}0.088{col 75}{space 4}-.0118748{col 88}{space 3} .1711594
{txt}{space 33} {c |}
{space 27}gender {c |}
{space 26}Female  {c |}{col 35}{res}{space 2}-.4209713{col 47}{space 2} .3666678{col 58}{space 1}   -1.15{col 67}{space 3}0.251{col 75}{space 4}-1.139627{col 88}{space 3} .2976843
{txt}{space 33} {c |}
{space 29}race {c |}
{space 27}Black  {c |}{col 35}{res}{space 2}-.9139402{col 47}{space 2} .6507431{col 58}{space 1}   -1.40{col 67}{space 3}0.160{col 75}{space 4}-2.189373{col 88}{space 3} .3614928
{txt}{space 27}Other  {c |}{col 35}{res}{space 2}-.9258611{col 47}{space 2} .5121111{col 58}{space 1}   -1.81{col 67}{space 3}0.071{col 75}{space 4} -1.92958{col 88}{space 3} .0778582
{txt}{space 33} {c |}
{space 25}employed {c |}
{space 22}Unemployed  {c |}{col 35}{res}{space 2}-.2965493{col 47}{space 2} .8375468{col 58}{space 1}   -0.35{col 67}{space 3}0.723{col 75}{space 4}-1.938111{col 88}{space 3} 1.345012
{txt}{space 25}Retired  {c |}{col 35}{res}{space 2}  .924633{col 47}{space 2} .5799791{col 58}{space 1}    1.59{col 67}{space 3}0.111{col 75}{space 4}-.2121051{col 88}{space 3} 2.061371
{txt}Perm. Disabled/Homemaker/Student  {c |}{col 35}{res}{space 2}-.0931599{col 47}{space 2} .4290736{col 58}{space 1}   -0.22{col 67}{space 3}0.828{col 75}{space 4}-.9341287{col 88}{space 3}  .747809
{txt}{space 33} {c |}
{space 26}marital {c |}
{space 25}Married  {c |}{col 35}{res}{space 2} .4275393{col 47}{space 2} .3660811{col 58}{space 1}    1.17{col 67}{space 3}0.243{col 75}{space 4}-.2899664{col 88}{space 3} 1.145045
{txt}{space 29}nfc1 {c |}{col 35}{res}{space 2} .4075396{col 47}{space 2} .3104414{col 58}{space 1}    1.31{col 67}{space 3}0.189{col 75}{space 4}-.2009144{col 88}{space 3} 1.015994
{txt}{space 24}evaluate1 {c |}{col 35}{res}{space 2}-.7508774{col 47}{space 2} .4116286{col 58}{space 1}   -1.82{col 67}{space 3}0.068{col 75}{space 4}-1.557655{col 88}{space 3} .0558999
{txt}{hline 34}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                            /cut1 {c |}{col 35}{res}{space 2}-1.381291{col 47}{space 2} 1.379494{col 75}{space 4} -4.08505{col 88}{space 3} 1.322468
{txt}                            /cut2 {c |}{col 35}{res}{space 2} 1.137005{col 47}{space 2} 1.350215{col 75}{space 4}-1.509369{col 88}{space 3} 3.783378
{txt}{hline 34}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-182.52901}  
Iteration 1:{space 3}log pseudolikelihood = {res:-157.48776}  
Iteration 2:{space 3}log pseudolikelihood = {res: -154.2702}  
Iteration 3:{space 3}log pseudolikelihood = {res: -154.2227}  
Iteration 4:{space 3}log pseudolikelihood = {res:-154.22263}  
Iteration 5:{space 3}log pseudolikelihood = {res:-154.22263}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       369
{txt}{col 51}Wald chi2({res}18{txt}){col 67}= {res}     61.08
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-154.22263{txt}{col 51}Pseudo R2{col 67}= {res}    0.1551

{txt}{hline 34}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 35}{c |}{col 47}    Robust
{col 1}                       econ_post3{col 35}{c |}      Coef.{col 47}   Std. Err.{col 59}      z{col 67}   P>|z|{col 75}     [95% Con{col 88}f. Interval]
{hline 34}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 25}partisan {c |}
{space 21}In-Partisan  {c |}{col 35}{res}{space 2} 2.396584{col 47}{space 2} .4514637{col 58}{space 1}    5.31{col 67}{space 3}0.000{col 75}{space 4} 1.511731{col 88}{space 3} 3.281437
{txt}{space 13}disagree_total_knowl {c |}{col 35}{res}{space 2}  .002721{col 47}{space 2} .0578416{col 58}{space 1}    0.05{col 67}{space 3}0.962{col 75}{space 4}-.1106465{col 88}{space 3} .1160884
{txt}{space 33} {c |}
{space 2}partisan#c.disagree_total_knowl {c |}
{space 21}In-Partisan  {c |}{col 35}{res}{space 2} .0981019{col 47}{space 2} .0778576{col 58}{space 1}    1.26{col 67}{space 3}0.208{col 75}{space 4}-.0544963{col 88}{space 3}    .2507
{txt}{space 33} {c |}
{space 27}names1 {c |}{col 35}{res}{space 2} .1138915{col 47}{space 2} .1655041{col 58}{space 1}    0.69{col 67}{space 3}0.491{col 75}{space 4}-.2104906{col 88}{space 3} .4382737
{txt}{space 23}disc_knowl {c |}{col 35}{res}{space 2}-.1642819{col 47}{space 2}  .449233{col 58}{space 1}   -0.37{col 67}{space 3}0.715{col 75}{space 4}-1.044762{col 88}{space 3} .7161986
{txt}{space 25}interest {c |}{col 35}{res}{space 2} .4421039{col 47}{space 2} .2541424{col 58}{space 1}    1.74{col 67}{space 3}0.082{col 75}{space 4}-.0560061{col 88}{space 3} .9402139
{txt}{space 30}age {c |}{col 35}{res}{space 2} -.003876{col 47}{space 2} .0141958{col 58}{space 1}   -0.27{col 67}{space 3}0.785{col 75}{space 4}-.0316991{col 88}{space 3} .0239472
{txt}{space 29}educ {c |}{col 35}{res}{space 2} .2091482{col 47}{space 2} .2202115{col 58}{space 1}    0.95{col 67}{space 3}0.342{col 75}{space 4}-.2224584{col 88}{space 3} .6407549
{txt}{space 27}income {c |}{col 35}{res}{space 2} .0786475{col 47}{space 2} .0464392{col 58}{space 1}    1.69{col 67}{space 3}0.090{col 75}{space 4}-.0123716{col 88}{space 3} .1696666
{txt}{space 33} {c |}
{space 27}gender {c |}
{space 26}Female  {c |}{col 35}{res}{space 2}-.4152575{col 47}{space 2} .3644506{col 58}{space 1}   -1.14{col 67}{space 3}0.255{col 75}{space 4}-1.129567{col 88}{space 3} .2990525
{txt}{space 33} {c |}
{space 29}race {c |}
{space 27}Black  {c |}{col 35}{res}{space 2}  -.91067{col 47}{space 2} .6581133{col 58}{space 1}   -1.38{col 67}{space 3}0.166{col 75}{space 4}-2.200548{col 88}{space 3} .3792083
{txt}{space 27}Other  {c |}{col 35}{res}{space 2}-.9192869{col 47}{space 2} .5098328{col 58}{space 1}   -1.80{col 67}{space 3}0.071{col 75}{space 4}-1.918541{col 88}{space 3} .0799671
{txt}{space 33} {c |}
{space 25}employed {c |}
{space 22}Unemployed  {c |}{col 35}{res}{space 2}-.3557468{col 47}{space 2}  .855004{col 58}{space 1}   -0.42{col 67}{space 3}0.677{col 75}{space 4}-2.031524{col 88}{space 3}  1.32003
{txt}{space 25}Retired  {c |}{col 35}{res}{space 2}  .895555{col 47}{space 2} .5770583{col 58}{space 1}    1.55{col 67}{space 3}0.121{col 75}{space 4}-.2354585{col 88}{space 3} 2.026568
{txt}Perm. Disabled/Homemaker/Student  {c |}{col 35}{res}{space 2}-.0998481{col 47}{space 2} .4265179{col 58}{space 1}   -0.23{col 67}{space 3}0.815{col 75}{space 4}-.9358078{col 88}{space 3} .7361116
{txt}{space 33} {c |}
{space 26}marital {c |}
{space 25}Married  {c |}{col 35}{res}{space 2} .4304564{col 47}{space 2}  .364101{col 58}{space 1}    1.18{col 67}{space 3}0.237{col 75}{space 4}-.2831686{col 88}{space 3} 1.144081
{txt}{space 29}nfc1 {c |}{col 35}{res}{space 2} .4148275{col 47}{space 2} .3063809{col 58}{space 1}    1.35{col 67}{space 3}0.176{col 75}{space 4}-.1856681{col 88}{space 3} 1.015323
{txt}{space 24}evaluate1 {c |}{col 35}{res}{space 2}-.7248227{col 47}{space 2} .4091124{col 58}{space 1}   -1.77{col 67}{space 3}0.076{col 75}{space 4}-1.526668{col 88}{space 3} .0770229
{txt}{hline 34}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                            /cut1 {c |}{col 35}{res}{space 2}-1.399895{col 47}{space 2}   1.3744{col 75}{space 4} -4.09367{col 88}{space 3} 1.293879
{txt}                            /cut2 {c |}{col 35}{res}{space 2} 1.111195{col 47}{space 2} 1.340187{col 75}{space 4}-1.515523{col 88}{space 3} 3.737913
{txt}{hline 34}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{com}.         
. esttab using 2000_ALTMEASURES_ECON.rtf, onecell nobaselevels replace  label pr2 aic bic se star(+ 0.10 * 0.05 ** 0.01) ///
>         mtitles("Original Measure" "/(D+A)" "Weight: Disc. Freq" "Weight: Disc Knowl.")  ///
>         title({c -(}\b Table XX.{c )-} "Alternative Measures of Disagreement - 2000 ANES (Knowledge)") ///
>         rename(disagree_total Disagreement disagree_avg Disagreement disagree_total_freq Disagreement ///
>                                 disagree_total_knowl Disagreement ///
>         1.partisan#c.disagree_total Party*Disagreement 1.partisan#c.disagree_avg Party*Disagreement ///
>                 1.partisan#c.disagree_total_freq Party*Disagreement 1.partisan#c.disagree_total_knowl Party*Disagreement)
{res}{txt}(output written to {browse  `"2000_ALTMEASURES_ECON.rtf"'})

{com}.                                 
. eststo clear
{txt}
{com}. 
. 
. 
. /****************************************
> *****************************************
>                 2002 ANES
> *****************************************
> ****************************************/                                
.                 
. *data cleaning
. clear
{txt}
{com}. do "Data Cleaning - 2002 ANES.do"
{txt}
{com}. **********************************************************************
. **********************************************************************
. ***********************2002 ANES Panel********************************
. **********************************************************************
. **********************************************************************
. **********************************************************************
. 
. 
. **********************************************************************
. ****************************Data Cleaning****************************
. **********************************************************************
. **********************************************************************
. **********************************************************************
. **********************************************************************
. 
. clear
{txt}
{com}. set more off
{txt}
{com}. use "ANES2002Panel.dta"
{txt}
{com}. set more off
{txt}
{com}. 
.                 ************************************
.                 *********Panels*********************
.                 ************************************
. 
. *We are most interested in those that participated in the 
. *2000TS Post-Election Wave (where the networks data was recorded
. *And those that did the 2002 Pre-Election Wave 
. *where the econ data was recorded
. 
. tab Waves

                    {txt}Waves Participation {c |}      Freq.     Percent        Cum.
{hline 40}{c +}{hline 35}
                           01. 2000 Pre {c |}{res}        152        8.41        8.41
{txt}               02. 2000 Pre - 2000 Post {c |}{res}        468       25.90       34.31
{txt}    03. 2000 Pre -           - 2002 Pre {c |}{res}         17        0.94       35.25
{txt}04. 2000 Pre -           - 2002 Pre - 2 {c |}{res}         29        1.60       36.86
{txt}    05. 2000 Pre - 2000 Post - 2002 Pre {c |}{res}         57        3.15       40.01
{txt}06. 2000 Pre - 2000 Post - 2002 Pre - 2 {c |}{res}        244       13.50       53.51
{txt}07. 2000 Pre - --------- - 2002 Pre - - {c |}{res}          5        0.28       53.79
{txt}08. 2000 Pre - --------- - 2002 Pre - 2 {c |}{res}         49        2.71       56.50
{txt}09. 2000 Pre - 2000 Post - 2002 Pre - - {c |}{res}         38        2.10       58.61
{txt}10. 2000 Pre - 2000 Post - 2002 Pre - 2 {c |}{res}        748       41.39      100.00
{txt}{hline 40}{c +}{hline 35}
                                  Total {c |}{res}      1,807      100.00
{txt}
{com}.         *10 = 2000 Pre, 2000 Post, 2002 Pre, 2002 Post, 2004 Post
.         *9 = 2000 Pre, 2000 Post, 2002 Pre, 2004 Post
.         *8 = 2000 Pre, 2002 Pre, 2002 Post, 20004 Post
.         *7 = 2000 Pre, 2002 Pre, 2004 Post
.         *6 = 2000 Pre, 2000 Post, 2002 Pre, 2002 Post
.         *5 = 2000 Pre, 2000 Post, 2002 Pre
.         *4 = 2000 Pre, 2002 Pre, 2002 Post
.         *3 = 2000 Pre, 2002 Pre 
.         *2 = 2000 Pre, 2000 Post
.         *1 = 2000 Pre
.         
. *thus, we are interested i: 10, 9, 6, 5 
. 
. gen panel = . 
{txt}(1807 missing values generated)

{com}. replace panel = 1 if Waves == 10
{txt}(748 real changes made)

{com}. replace panel = 1 if Waves == 9
{txt}(38 real changes made)

{com}. replace panel = 1 if Waves == 6
{txt}(244 real changes made)

{com}. replace panel = 1 if Waves == 5
{txt}(57 real changes made)

{com}. replace panel = 0 if panel == .
{txt}(720 real changes made)

{com}. label def pan 1 "2000 Post & 2002 Pre" 
{txt}
{com}. label values panel pan
{txt}
{com}.         
.                 ************************************
.                 *********Economic Assessments*******
.                 ************************************
.                 
. recode M000491 (1=5) (2=4) (3=3) (4=2) (5=1), gen(retro2002)
{txt}(1001 differences between M000491 and retro2002)

{com}. label def ret 1 "Much Worse" 2 "Somewhat Worse" 3 "Same" 4 "Somewhat Better" 5 "Much Better"
{txt}
{com}. label values retro2002 ret
{txt}
{com}. label var retro2002 "Retro (2002)"
{txt}
{com}. tab retro if panel == 1

   {txt}Retro (2002) {c |}      Freq.     Percent        Cum.
{hline 16}{c +}{hline 35}
     Much Worse {c |}{res}        133       12.34       12.34
{txt} Somewhat Worse {c |}{res}         48        4.45       16.79
{txt}           Same {c |}{res}        493       45.73       62.52
{txt}Somewhat Better {c |}{res}        262       24.30       86.83
{txt}    Much Better {c |}{res}        142       13.17      100.00
{txt}{hline 16}{c +}{hline 35}
          Total {c |}{res}      1,078      100.00
{txt}
{com}. 
. recode retro (1=1) (2=1) (3=2) (4=3) (5=3), gen(retro2002_3)
{txt}(1554 differences between retro2002 and retro2002_3)

{com}. label def ret1 1 "Worse" 2 "Same" 3 "Better" 
{txt}
{com}. label values retro2002_3 ret1 
{txt}
{com}. label var retro2002_3 "Retro (2002; 3-pt)"
{txt}
{com}. tab retro2002_3

      {txt}Retro {c |}
     (2002; {c |}
      3-pt) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
      Worse {c |}{res}        302       16.91       16.91
{txt}       Same {c |}{res}        785       43.95       60.86
{txt}     Better {c |}{res}        699       39.14      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,786      100.00
{txt}
{com}. 
. gen version = . 
{txt}(1807 missing values generated)

{com}. replace version = 1 if M000488a !=.
{txt}(896 real changes made)

{com}. replace version = 0 if M000488b !=.
{txt}(891 real changes made)

{com}. label def ver 1 "Better" 0 "Worse"
{txt}
{com}. label values version ver
{txt}
{com}. label var version "Version of Retro2002"
{txt}
{com}. 
. tab retro2002 version if panel == 1, row col chi2
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

                {c |} Version of Retro2002
   Retro (2002) {c |}     Worse     Better {c |}     Total
{hline 16}{c +}{hline 22}{c +}{hline 10}
     Much Worse {c |}{res}        69         64 {txt}{c |}{res}       133 
                {txt}{c |}{res}     51.88      48.12 {txt}{c |}{res}    100.00 
                {txt}{c |}{res}     12.71      11.96 {txt}{c |}{res}     12.34 
{txt}{hline 16}{c +}{hline 22}{c +}{hline 10}
 Somewhat Worse {c |}{res}        24         24 {txt}{c |}{res}        48 
                {txt}{c |}{res}     50.00      50.00 {txt}{c |}{res}    100.00 
                {txt}{c |}{res}      4.42       4.49 {txt}{c |}{res}      4.45 
{txt}{hline 16}{c +}{hline 22}{c +}{hline 10}
           Same {c |}{res}       256        237 {txt}{c |}{res}       493 
                {txt}{c |}{res}     51.93      48.07 {txt}{c |}{res}    100.00 
                {txt}{c |}{res}     47.15      44.30 {txt}{c |}{res}     45.73 
{txt}{hline 16}{c +}{hline 22}{c +}{hline 10}
Somewhat Better {c |}{res}       139        123 {txt}{c |}{res}       262 
                {txt}{c |}{res}     53.05      46.95 {txt}{c |}{res}    100.00 
                {txt}{c |}{res}     25.60      22.99 {txt}{c |}{res}     24.30 
{txt}{hline 16}{c +}{hline 22}{c +}{hline 10}
    Much Better {c |}{res}        55         87 {txt}{c |}{res}       142 
                {txt}{c |}{res}     38.73      61.27 {txt}{c |}{res}    100.00 
                {txt}{c |}{res}     10.13      16.26 {txt}{c |}{res}     13.17 
{txt}{hline 16}{c +}{hline 22}{c +}{hline 10}
          Total {c |}{res}       543        535 {txt}{c |}{res}     1,078 
                {txt}{c |}{res}     50.37      49.63 {txt}{c |}{res}    100.00 
                {txt}{c |}{res}    100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}4{txt}) = {res}  9.0497  {txt} Pr = {res}0.060
{txt}
{com}. tab retro2002_3 version if panel == 1, row col chi2
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

     Retro {c |}
    (2002; {c |} Version of Retro2002
     3-pt) {c |}     Worse     Better {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
     Worse {c |}{res}        93         88 {txt}{c |}{res}       181 
           {txt}{c |}{res}     51.38      48.62 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     17.13      16.45 {txt}{c |}{res}     16.79 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
      Same {c |}{res}       256        237 {txt}{c |}{res}       493 
           {txt}{c |}{res}     51.93      48.07 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     47.15      44.30 {txt}{c |}{res}     45.73 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
    Better {c |}{res}       194        210 {txt}{c |}{res}       404 
           {txt}{c |}{res}     48.02      51.98 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     35.73      39.25 {txt}{c |}{res}     37.48 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       543        535 {txt}{c |}{res}     1,078 
           {txt}{c |}{res}     50.37      49.63 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}    100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}2{txt}) = {res}  1.4447  {txt} Pr = {res}0.486
{txt}
{com}. ttest retro2002 if panel == 1, by(version)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
   Worse {c |}{res}{col 12}    543{col 22} 3.160221{col 34} .0468167{col 46}  1.09094{col 58} 3.068257{col 70} 3.252185
  {txt}Better {c |}{res}{col 12}    535{col 22} 3.271028{col 34} .0499116{col 46} 1.154458{col 58} 3.172981{col 70} 3.369075
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}   1078{col 22} 3.215213{col 34} .0342266{col 46} 1.123758{col 58} 3.148055{col 70} 3.282372
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} -.110807{col 34} .0684035{col 58}-.2450264{col 70} .0234123
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}Worse{txt}) - mean({res}Better{txt})                             t = {res} -1.6199
{txt}Ho: diff = 0                                     degrees of freedom = {res}    1076

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.0528         {txt}Pr(|T| > |t|) = {res}0.1055          {txt}Pr(T > t) = {res}0.9472
{txt}
{com}. ttest retro2002_3 if panel == 1, by(version)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
   Worse {c |}{res}{col 12}    543{col 22} 2.186004{col 34} .0301885{col 46} .7034622{col 58} 2.126703{col 70} 2.245304
  {txt}Better {c |}{res}{col 12}    535{col 22} 2.228037{col 34} .0307524{col 46} .7113044{col 58} 2.167627{col 70} 2.288448
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}   1078{col 22} 2.206865{col 34} .0215439{col 46} .7073491{col 58} 2.164592{col 70} 2.249137
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}-.0420337{col 34}   .04309{col 58}-.1265836{col 70} .0425162
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}Worse{txt}) - mean({res}Better{txt})                             t = {res} -0.9755
{txt}Ho: diff = 0                                     degrees of freedom = {res}    1076

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.1648         {txt}Pr(|T| > |t|) = {res}0.3295          {txt}Pr(T > t) = {res}0.8352
{txt}
{com}. 
. 
.                 ************************************
.                 *********Partisanship***************
.                 ************************************
.                 
. *2002 Pre-Election Wave
.         *7-pt
.         recode M023038X (0=1) (1=2) (2=3) (3=4) (4=5) (5=6) (6=7), gen(partyid)
{txt}(1167 differences between M023038X and partyid)

{com}.         label def pi 1 "St. Dem" 2 "Weak Dem" 3 "Lean Dem" 4" Ind." 5 "Lean Rep" 6 "Weak Rep" 7 "St. Rep"
{txt}
{com}.         label var partyid "Party Identification (2002)"
{txt}
{com}.         label values partyid pi
{txt}
{com}.         *3-Point Categorical
.         gen pid_3 = . 
{txt}(1807 missing values generated)

{com}.         replace pid_3 = 1 if partyid >=1 & partyid <= 3
{txt}(554 real changes made)

{com}.         replace pid_3 = 3 if partyid == 4
{txt}(78 real changes made)

{com}.         replace pid_3 = 2 if partyid >=5 & partyid <= 7
{txt}(535 real changes made)

{com}.         label var pid_3 "Party ID (2002)"
{txt}
{com}.         label def pi2 1 "Democrat" 3 "Independent" 2 "Republican"
{txt}
{com}.         label values pid_3 pi2
{txt}
{com}.         
.         *Republican Vs. Democrat
.         gen pid_2 = . 
{txt}(1807 missing values generated)

{com}.         replace pid_2 = 1 if partyid >=1 & partyid <= 3
{txt}(554 real changes made)

{com}.         replace pid_2 = 0 if partyid >=5 & partyid <= 7
{txt}(535 real changes made)

{com}.         label var pid_2 "PID (2002)" 
{txt}
{com}.         label def pi3 1 "Democrat" 0 "Republican"
{txt}
{com}.         label values pid_2 pi3
{txt}
{com}.         
.         tab partyid

      {txt}Party {c |}
Identificat {c |}
 ion (2002) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
    St. Dem {c |}{res}        195       16.71       16.71
{txt}   Weak Dem {c |}{res}        195       16.71       33.42
{txt}   Lean Dem {c |}{res}        164       14.05       47.47
{txt}       Ind. {c |}{res}         78        6.68       54.16
{txt}   Lean Rep {c |}{res}        161       13.80       67.95
{txt}   Weak Rep {c |}{res}        183       15.68       83.63
{txt}    St. Rep {c |}{res}        191       16.37      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,167      100.00
{txt}
{com}.         tab pid_3

   {txt}Party ID {c |}
     (2002) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
   Democrat {c |}{res}        554       47.47       47.47
{txt} Republican {c |}{res}        535       45.84       93.32
{txt}Independent {c |}{res}         78        6.68      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,167      100.00
{txt}
{com}.         tab pid_2

 {txt}PID (2002) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
 Republican {c |}{res}        535       49.13       49.13
{txt}   Democrat {c |}{res}        554       50.87      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,089      100.00
{txt}
{com}.         
.         *PID Str
.         gen pid_str = . 
{txt}(1807 missing values generated)

{com}.         replace pid_str = 1 if partyid == 3
{txt}(164 real changes made)

{com}.         replace pid_str = 1 if partyid == 5
{txt}(161 real changes made)

{com}.         replace pid_str = 2 if partyid == 2
{txt}(195 real changes made)

{com}.         replace pid_str = 2 if partyid == 6
{txt}(183 real changes made)

{com}.         replace pid_str = 3 if partyid == 1
{txt}(195 real changes made)

{com}.         replace pid_str = 3 if partyid == 7
{txt}(191 real changes made)

{com}.         label var pid_str "PID Str."
{txt}
{com}.         label def pi4 1 "Leaner" 2 "Weak" 3 "Strong"
{txt}
{com}.         label values pid_str pi4
{txt}
{com}.         
.         summ pid_str 

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 5}pid_str {c |}{res}      1089    2.056015    .8064441          1          3
{txt}
{com}.         gen pid_str01 = (pid_str - r(min))/(r(max)-r(min))
{txt}(718 missing values generated)

{com}.         label var pid_str01 "PID Str"
{txt}
{com}.         
.         recode partyid (1=4) (2=3) (3=2) (4=1) ///
>                 (5=2) (6=3) (7=4), gen(pid_str_full)
{txt}(1167 differences between partyid and pid_str_full)

{com}.         
.         label var pid_str_full "PID Str."
{txt}
{com}.         label def pi5 1 "Pure Ind." 2 "Leaner" 3 "Weak" 4 "Strong"
{txt}
{com}.         label values pid_str pi5
{txt}
{com}.         
.         *Partisan*
.         recode pid_2 (1=0) (0=1), gen(partisan)
{txt}(1089 differences between pid_2 and partisan)

{com}.         label var partisan "Co-Partisan to Inc. President"
{txt}
{com}.         label def part1 1 "In-Partisan" 0 "Out-Partisan"
{txt}
{com}.         label values partisan part1
{txt}
{com}. 
. 
.         
. *2000 Pre-Election
.         *7pt
.         recode M000523 (0=1) (1=2) (2=3) (3=4) (4=5) (5=6) (6=7), gen(partyid_2000)
{txt}(1776 differences between M000523 and partyid_2000)

{com}.         label var partyid_2000 "Party Identification (2000)"
{txt}
{com}.         label values partyid_2000 pi
{txt}
{com}.         *3-pt
.         gen pid_32000 = . 
{txt}(1807 missing values generated)

{com}.         replace pid_32000 = 1 if partyid_2000 >=1 & partyid_2000 <= 3
{txt}(889 real changes made)

{com}.         replace pid_32000 = 3 if partyid_2000 == 4
{txt}(206 real changes made)

{com}.         replace pid_32000 = 2 if partyid_2000 >=5 & partyid_2000 <= 7
{txt}(690 real changes made)

{com}.         label var pid_3 "Party ID (2000)"
{txt}
{com}.         label values pid_3200 pi2
{txt}
{com}.         *Republican Vs. Democrat
.         gen pid_2_2000 = . 
{txt}(1807 missing values generated)

{com}.         replace pid_2_2000 = 1 if partyid_2000 >=1 & partyid_2000 <= 3
{txt}(889 real changes made)

{com}.         replace pid_2_2000 = 0 if partyid_2000 >=5 & partyid_2000 <= 7
{txt}(690 real changes made)

{com}.         label var pid_2_2000 "PID (2000)" 
{txt}
{com}.         label values pid_2_2000 pi3
{txt}
{com}.         
.         *Partisan
.         recode pid_2_2000 (1=0) (0=1), gen(partisan2000)
{txt}(1579 differences between pid_2_2000 and partisan2000)

{com}.         label values partisan2000 part1
{txt}
{com}.         label var partisan2000 "Co-Partisan to Inc. President (2000 Measure)"
{txt}
{com}.         
.         *Pid Str
.         gen pid_str2000 = . 
{txt}(1807 missing values generated)

{com}.         replace pid_str2000 = 1 if partyid_2000 == 3
{txt}(269 real changes made)

{com}.         replace pid_str2000 = 1 if partyid_2000 == 5
{txt}(230 real changes made)

{com}.         replace pid_str2000 = 2 if partyid_2000 == 2
{txt}(274 real changes made)

{com}.         replace pid_str2000 = 2 if partyid_2000 == 6
{txt}(215 real changes made)

{com}.         replace pid_str2000 = 3 if partyid_2000 == 1
{txt}(346 real changes made)

{com}.         replace pid_str2000 = 3 if partyid_2000 == 7
{txt}(245 real changes made)

{com}.         label var pid_str2000 "PID Str. (2000)"
{txt}
{com}.         label values pid_str2000 pi4
{txt}
{com}.         
.         summ pid_str2000

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 1}pid_str2000 {c |}{res}      1579    2.058265    .8290663          1          3
{txt}
{com}.         gen pid_str2000_01 = (pid_str2000 - r(min))/(r(max)-r(min))
{txt}(228 missing values generated)

{com}.         label var pid_str2000_01 "PID Str (2000)"
{txt}
{com}.         
. *Relationship
.         pwcorr partyid partyid_2000, sig

             {txt}{c |}  partyid partyi~0
{hline 13}{c +}{hline 18}
     partyid {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
partyid_2000 {c |} {res}  0.8442   1.0000 
             {txt}{c |}{res}   0.0000
             {txt}{c |}

{com}.         tab pid_3 pid_32000, row col chi2
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

   Party ID {c |}            pid_32000
     (2000) {c |}  Democrat  Republica  Independe {c |}     Total
{hline 12}{c +}{hline 33}{c +}{hline 10}
   Democrat {c |}{res}       486         32         32 {txt}{c |}{res}       550 
            {txt}{c |}{res}     88.36       5.82       5.82 {txt}{c |}{res}    100.00 
            {txt}{c |}{res}     86.63       6.67      27.35 {txt}{c |}{res}     47.50 
{txt}{hline 12}{c +}{hline 33}{c +}{hline 10}
 Republican {c |}{res}        53        428         50 {txt}{c |}{res}       531 
            {txt}{c |}{res}      9.98      80.60       9.42 {txt}{c |}{res}    100.00 
            {txt}{c |}{res}      9.45      89.17      42.74 {txt}{c |}{res}     45.85 
{txt}{hline 12}{c +}{hline 33}{c +}{hline 10}
Independent {c |}{res}        22         20         35 {txt}{c |}{res}        77 
            {txt}{c |}{res}     28.57      25.97      45.45 {txt}{c |}{res}    100.00 
            {txt}{c |}{res}      3.92       4.17      29.91 {txt}{c |}{res}      6.65 
{txt}{hline 12}{c +}{hline 33}{c +}{hline 10}
      Total {c |}{res}       561        480        117 {txt}{c |}{res}     1,158 
            {txt}{c |}{res}     48.45      41.45      10.10 {txt}{c |}{res}    100.00 
            {txt}{c |}{res}    100.00     100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}4{txt}) = {res}824.1181  {txt} Pr = {res}0.000
{txt}
{com}.         tab pid_2 pid_2_2000, row col chi2
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

           {c |}      PID (2000)
PID (2002) {c |} Republica   Democrat {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
Republican {c |}{res}       428         53 {txt}{c |}{res}       481 
           {txt}{c |}{res}     88.98      11.02 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     93.04       9.83 {txt}{c |}{res}     48.15 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
  Democrat {c |}{res}        32        486 {txt}{c |}{res}       518 
           {txt}{c |}{res}      6.18      93.82 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}      6.96      90.17 {txt}{c |}{res}     51.85 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       460        539 {txt}{c |}{res}       999 
           {txt}{c |}{res}     46.05      53.95 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}    100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}1{txt}) = {res}688.3242  {txt} Pr = {res}0.000
{txt}
{com}. 
. *Bivariate relationship with economic assessments? 
.         tab retro2002_3 pid_2 if panel == 1, row col chi2
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

     Retro {c |}
    (2002; {c |}      PID (2002)
     3-pt) {c |} Republica   Democrat {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
     Worse {c |}{res}       102         51 {txt}{c |}{res}       153 
           {txt}{c |}{res}     66.67      33.33 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     20.99      10.14 {txt}{c |}{res}     15.47 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
      Same {c |}{res}       253        200 {txt}{c |}{res}       453 
           {txt}{c |}{res}     55.85      44.15 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     52.06      39.76 {txt}{c |}{res}     45.80 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
    Better {c |}{res}       131        252 {txt}{c |}{res}       383 
           {txt}{c |}{res}     34.20      65.80 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     26.95      50.10 {txt}{c |}{res}     38.73 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       486        503 {txt}{c |}{res}       989 
           {txt}{c |}{res}     49.14      50.86 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}    100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}2{txt}) = {res} 61.1539  {txt} Pr = {res}0.000
{txt}
{com}.         tab retro2002_3 pid_2_2000 if panel == 1, row col chi2  
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

     Retro {c |}
    (2002; {c |}      PID (2000)
     3-pt) {c |} Republica   Democrat {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
     Worse {c |}{res}        95         53 {txt}{c |}{res}       148 
           {txt}{c |}{res}     64.19      35.81 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     21.35      10.39 {txt}{c |}{res}     15.50 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
      Same {c |}{res}       232        204 {txt}{c |}{res}       436 
           {txt}{c |}{res}     53.21      46.79 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     52.13      40.00 {txt}{c |}{res}     45.65 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
    Better {c |}{res}       118        253 {txt}{c |}{res}       371 
           {txt}{c |}{res}     31.81      68.19 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     26.52      49.61 {txt}{c |}{res}     38.85 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       445        510 {txt}{c |}{res}       955 
           {txt}{c |}{res}     46.60      53.40 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}    100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}2{txt}) = {res} 58.6889  {txt} Pr = {res}0.000
{txt}
{com}.         tab retro2002 pid_2 if panel == 1, row col chi2
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

                {c |}      PID (2002)
   Retro (2002) {c |} Republica   Democrat {c |}     Total
{hline 16}{c +}{hline 22}{c +}{hline 10}
     Much Worse {c |}{res}        77         36 {txt}{c |}{res}       113 
                {txt}{c |}{res}     68.14      31.86 {txt}{c |}{res}    100.00 
                {txt}{c |}{res}     15.84       7.16 {txt}{c |}{res}     11.43 
{txt}{hline 16}{c +}{hline 22}{c +}{hline 10}
 Somewhat Worse {c |}{res}        25         15 {txt}{c |}{res}        40 
                {txt}{c |}{res}     62.50      37.50 {txt}{c |}{res}    100.00 
                {txt}{c |}{res}      5.14       2.98 {txt}{c |}{res}      4.04 
{txt}{hline 16}{c +}{hline 22}{c +}{hline 10}
           Same {c |}{res}       253        200 {txt}{c |}{res}       453 
                {txt}{c |}{res}     55.85      44.15 {txt}{c |}{res}    100.00 
                {txt}{c |}{res}     52.06      39.76 {txt}{c |}{res}     45.80 
{txt}{hline 16}{c +}{hline 22}{c +}{hline 10}
Somewhat Better {c |}{res}        96        153 {txt}{c |}{res}       249 
                {txt}{c |}{res}     38.55      61.45 {txt}{c |}{res}    100.00 
                {txt}{c |}{res}     19.75      30.42 {txt}{c |}{res}     25.18 
{txt}{hline 16}{c +}{hline 22}{c +}{hline 10}
    Much Better {c |}{res}        35         99 {txt}{c |}{res}       134 
                {txt}{c |}{res}     26.12      73.88 {txt}{c |}{res}    100.00 
                {txt}{c |}{res}      7.20      19.68 {txt}{c |}{res}     13.55 
{txt}{hline 16}{c +}{hline 22}{c +}{hline 10}
          Total {c |}{res}       486        503 {txt}{c |}{res}       989 
                {txt}{c |}{res}     49.14      50.86 {txt}{c |}{res}    100.00 
                {txt}{c |}{res}    100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}4{txt}) = {res} 66.9199  {txt} Pr = {res}0.000
{txt}
{com}.         tab retro2002 pid_2_2000 if panel == 1, row col chi2    
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

                {c |}      PID (2000)
   Retro (2002) {c |} Republica   Democrat {c |}     Total
{hline 16}{c +}{hline 22}{c +}{hline 10}
     Much Worse {c |}{res}        71         39 {txt}{c |}{res}       110 
                {txt}{c |}{res}     64.55      35.45 {txt}{c |}{res}    100.00 
                {txt}{c |}{res}     15.96       7.65 {txt}{c |}{res}     11.52 
{txt}{hline 16}{c +}{hline 22}{c +}{hline 10}
 Somewhat Worse {c |}{res}        24         14 {txt}{c |}{res}        38 
                {txt}{c |}{res}     63.16      36.84 {txt}{c |}{res}    100.00 
                {txt}{c |}{res}      5.39       2.75 {txt}{c |}{res}      3.98 
{txt}{hline 16}{c +}{hline 22}{c +}{hline 10}
           Same {c |}{res}       232        204 {txt}{c |}{res}       436 
                {txt}{c |}{res}     53.21      46.79 {txt}{c |}{res}    100.00 
                {txt}{c |}{res}     52.13      40.00 {txt}{c |}{res}     45.65 
{txt}{hline 16}{c +}{hline 22}{c +}{hline 10}
Somewhat Better {c |}{res}        86        153 {txt}{c |}{res}       239 
                {txt}{c |}{res}     35.98      64.02 {txt}{c |}{res}    100.00 
                {txt}{c |}{res}     19.33      30.00 {txt}{c |}{res}     25.03 
{txt}{hline 16}{c +}{hline 22}{c +}{hline 10}
    Much Better {c |}{res}        32        100 {txt}{c |}{res}       132 
                {txt}{c |}{res}     24.24      75.76 {txt}{c |}{res}    100.00 
                {txt}{c |}{res}      7.19      19.61 {txt}{c |}{res}     13.82 
{txt}{hline 16}{c +}{hline 22}{c +}{hline 10}
          Total {c |}{res}       445        510 {txt}{c |}{res}       955 
                {txt}{c |}{res}     46.60      53.40 {txt}{c |}{res}    100.00 
                {txt}{c |}{res}    100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}4{txt}) = {res} 63.4213  {txt} Pr = {res}0.000
{txt}
{com}. 
. 
.                 *****************************************************
.                 *********Network Size and Disagreement***************
.                 *****************************************************
. *Network Size (# Listed Discussants)
. 
.         gen names = . 
{txt}(1807 missing values generated)

{com}.         replace names = 4 if M001702 == 1 & M001701 == 1 & M001700 == 1 & M001699 == 1
{txt}(327 real changes made)

{com}.         replace names = 3 if M001702 == 5 & M001701 == 1 & M001700 == 1 & M001699 == 1
{txt}(223 real changes made)

{com}.         replace names = 2 if M001701 == 5 & M001700 == 1 & M001699 == 1
{txt}(311 real changes made)

{com}.         replace names = 1 if M001700 == 5 & M001699 == 1
{txt}(290 real changes made)

{com}.         replace names = 0 if M001699 == 5
{txt}(399 real changes made)

{com}.         label var names "# Listed Disc."
{txt}
{com}.         
.         *Discussant Vote Choice
.                 foreach var in M001710 M001718 M001726 M001734 {c -(}
{txt}  2{com}.                         tab `var' if panel == 1
{txt}  3{com}.                 {c )-}

{txt}Z12. How name 1 voted in election {c |}      Freq.     Percent        Cum.
{hline 34}{c +}{hline 35}
                       1. AL GORE {c |}{res}        344       42.52       42.52
{txt}                 3. GEORGE W BUSH {c |}{res}        359       44.38       86.90
{txt}5. SOME OTHER CANDIDATE (SPECIFY) {c |}{res}         34        4.20       91.10
{txt}                   7. DIDN'T VOTE {c |}{res}         62        7.66       98.76
{txt}      8. INELIGIBLE TO VOTE [VOL] {c |}{res}         10        1.24      100.00
{txt}{hline 34}{c +}{hline 35}
                            Total {c |}{res}        809      100.00

{txt}Z16. How name 2 voted in election {c |}      Freq.     Percent        Cum.
{hline 34}{c +}{hline 35}
                       1. AL GORE {c |}{res}        255       43.29       43.29
{txt}                 3. GEORGE W BUSH {c |}{res}        255       43.29       86.59
{txt}5. SOME OTHER CANDIDATE [SPECIFY] {c |}{res}         23        3.90       90.49
{txt}                   7. DIDN'T VOTE {c |}{res}         49        8.32       98.81
{txt}      8. INELIGIBLE TO VOTE [VOL] {c |}{res}          7        1.19      100.00
{txt}{hline 34}{c +}{hline 35}
                            Total {c |}{res}        589      100.00

      {txt}Z20. How name 3 voted in election {c |}      Freq.     Percent        Cum.
{hline 40}{c +}{hline 35}
                             1. AL GORE {c |}{res}        156       41.38       41.38
{txt}                       3. GEORGE W BUSH {c |}{res}        179       47.48       88.86
{txt}5. SOME OTHER CANDIDATE (SPECIFY) [SPEC {c |}{res}          7        1.86       90.72
{txt}                         7. DIDN'T VOTE {c |}{res}         30        7.96       98.67
{txt}            8. INELIGIBLE TO VOTE [VOL] {c |}{res}          5        1.33      100.00
{txt}{hline 40}{c +}{hline 35}
                                  Total {c |}{res}        377      100.00

{txt}Z24. How name 4 voted in election {c |}      Freq.     Percent        Cum.
{hline 34}{c +}{hline 35}
                       1. AL GORE {c |}{res}         82       37.10       37.10
{txt}                 3. GEORGE W BUSH {c |}{res}        109       49.32       86.43
{txt}5. SOME OTHER CANDIDATE (SPECIFY) {c |}{res}          6        2.71       89.14
{txt}                   7. DIDN'T VOTE {c |}{res}         20        9.05       98.19
{txt}      8. INELIGIBLE TO VOTE [VOL] {c |}{res}          4        1.81      100.00
{txt}{hline 34}{c +}{hline 35}
                            Total {c |}{res}        221      100.00
{txt}
{com}.                 
.                 *1 = Al Gore
.                 *3 = George Bush
.                 *5 = Some Other Candidate
.                 *7 = Didn't vote
.                 *8 = ineliglb to vote
.                 *98 = dk
.                 *99 = ref
.         
.         recode names (0=.), gen(names1)
{txt}(399 differences between names and names1)

{com}.         label var names1 "Network Size"
{txt}
{com}.         summ names1

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 6}names1 {c |}{res}      1151    2.509991    1.150287          1          4
{txt}
{com}.         gen numgiven01 = (names1 - r(min))/(r(max)-r(min))
{txt}(656 missing values generated)

{com}.         label var numgiven01 "Network Size"
{txt}
{com}.         
.                 
.         *Respondent vote choice*
.                         tab M001249

          {txt}C6. R vote cast for President {c |}      Freq.     Percent        Cum.
{hline 40}{c +}{hline 35}
                            1. AL GORE  {c |}{res}        590       50.77       50.77
{txt}2. HOWARD PHILLIPS-CONSTITUTION PARTY C {c |}{res}          1        0.09       50.86
{txt}                      3. GEORGE W. BUSH {c |}{res}        530       45.61       96.47
{txt}   4. HARRY BROWN-LIBERTARIAN CANDIDATE {c |}{res}          4        0.34       96.82
{txt}                       5. PAT BUCHANAN  {c |}{res}          3        0.26       97.07
{txt}                         6. RALPH NADER {c |}{res}         33        2.84       99.91
{txt}           7. R REPORTS VOTING FOR SELF {c |}{res}          1        0.09      100.00
{txt}{hline 40}{c +}{hline 35}
                                  Total {c |}{res}      1,162      100.00
{txt}
{com}.                         *1 = Gore
.                         *2 = howard philips (n = 1)
.                         *3 = Bush
.                         *4 = Libertarian (n=4)
.                         *5 = Pat Buchanan (n =3)
.                         *6 = Nader (n=33)
.                         *7 = reports voting for self
.                         *0 = NA/INAP
.                         
.                                 *0 = NA/INAP
.         
. *Disagreement
.         *need to create indices for 'agreement' and 'disagreement'
.         *agreement: gore/gore, bush/bush; 
.         *disagree: gore/bush, bush/gore, nader/gore, nader/bush, gore/other, bush/other, remainder 3rd party/bush or gore
.                 
.         *Agree = 1, Disagree = 0
.                 label def agr 1 "Agree" 0 "Disagree" 
{txt}
{com}.                 gen d1_votea = . 
{txt}(1807 missing values generated)

{com}.                 replace d1_votea = 1 if M001249 == 1 & M001710 == 1
{txt}(301 real changes made)

{com}.                 replace d1_votea = 1 if M001249 == 3 & M001710 == 3
{txt}(307 real changes made)

{com}.                 replace d1_votea = 0  if M001249 == 1 & M001710 == 3
{txt}(92 real changes made)

{com}.                 replace d1_votea = 0  if M001249 == 3 & M001710 == 1
{txt}(77 real changes made)

{com}.                 replace d1_votea = 0  if M001249 == 1 & M001710 == 5
{txt}(11 real changes made)

{com}.                 replace d1_votea = 0  if M001249 == 3 & M001710 == 5
{txt}(9 real changes made)

{com}.                 replace d1_votea = 0  if M001249 == 2 & M001710 == 1
{txt}(0 real changes made)

{com}.                 replace d1_votea = 0  if M001249 == 2 & M001710 == 3
{txt}(1 real change made)

{com}.                 replace d1_votea = 0  if M001249 == 4 & M001710 == 1
{txt}(2 real changes made)

{com}.                 replace d1_votea = 0  if M001249 == 4 & M001710 == 3
{txt}(1 real change made)

{com}.                 replace d1_votea = 0  if M001249 == 5 & M001710 == 1
{txt}(0 real changes made)

{com}.                 replace d1_votea = 0  if M001249 == 5 & M001710 == 3
{txt}(0 real changes made)

{com}.                 replace d1_votea = 0  if M001249 == 6 & M001710 == 1
{txt}(10 real changes made)

{com}.                 replace d1_votea = 0  if M001249 == 6 & M001710 == 3
{txt}(11 real changes made)

{com}.                 label var d1_votea "D1 Vote Agreement"
{txt}
{com}.                 label values d1_votea agr
{txt}
{com}.                 
.                 
.                 gen d2_votea = . 
{txt}(1807 missing values generated)

{com}.                 replace d2_votea = 1 if M001249 == 1 & M001718 == 1
{txt}(226 real changes made)

{com}.                 replace d2_votea = 1 if M001249 == 3 & M001718 == 3
{txt}(221 real changes made)

{com}.                 replace d2_votea = 0  if M001249 == 1 & M001718 == 3
{txt}(73 real changes made)

{com}.                 replace d2_votea = 0  if M001249 == 3 & M001718 == 1
{txt}(62 real changes made)

{com}.                 replace d2_votea = 0  if M001249 == 1 & M001718 == 5
{txt}(10 real changes made)

{com}.                 replace d2_votea = 0  if M001249 == 3 & M001718 == 5
{txt}(8 real changes made)

{com}.                 replace d2_votea = 0  if M001249 == 2 & M001718 == 1
{txt}(0 real changes made)

{com}.                 replace d2_votea = 0  if M001249 == 2 & M001718 == 3
{txt}(0 real changes made)

{com}.                 replace d2_votea = 0  if M001249 == 4 & M001718 == 1
{txt}(2 real changes made)

{com}.                 replace d2_votea = 0  if M001249 == 4 & M001718 == 3
{txt}(0 real changes made)

{com}.                 replace d2_votea = 0  if M001249 == 5 & M001718 == 1
{txt}(1 real change made)

{com}.                 replace d2_votea = 0  if M001249 == 5 & M001718 == 3
{txt}(0 real changes made)

{com}.                 replace d2_votea = 0  if M001249 == 6 & M001718 == 1
{txt}(10 real changes made)

{com}.                 replace d2_votea = 0  if M001249 == 6 & M001718 == 3
{txt}(5 real changes made)

{com}.                 label var d2_votea "D2 Vote Agreement"
{txt}
{com}.                 label values d2_votea agr
{txt}
{com}.                 
.                                 
.                 gen d3_votea = . 
{txt}(1807 missing values generated)

{com}.                 replace d3_votea = 1 if M001249 == 1 & M001726 == 1
{txt}(137 real changes made)

{com}.                 replace d3_votea = 1 if M001249 == 3 & M001726 == 3
{txt}(149 real changes made)

{com}.                 replace d3_votea = 0  if M001249 == 1 & M001726 == 3
{txt}(53 real changes made)

{com}.                 replace d3_votea = 0  if M001249 == 3 & M001726 == 1
{txt}(43 real changes made)

{com}.                 replace d3_votea = 0  if M001249 == 1 & M001726 == 5
{txt}(3 real changes made)

{com}.                 replace d3_votea = 0  if M001249 == 3 & M001726 == 5
{txt}(4 real changes made)

{com}.                 replace d3_votea = 0  if M001249 == 2 & M001726 == 1
{txt}(0 real changes made)

{com}.                 replace d3_votea = 0  if M001249 == 2 & M001726 == 3
{txt}(1 real change made)

{com}.                 replace d3_votea = 0  if M001249 == 4 & M001726 == 1
{txt}(1 real change made)

{com}.                 replace d3_votea = 0  if M001249 == 4 & M001726 == 3
{txt}(1 real change made)

{com}.                 replace d3_votea = 0  if M001249 == 5 & M001726 == 1
{txt}(0 real changes made)

{com}.                 replace d3_votea = 0  if M001249 == 5 & M001726 == 3
{txt}(0 real changes made)

{com}.                 replace d3_votea = 0  if M001249 == 6 & M001726 == 1
{txt}(4 real changes made)

{com}.                 replace d3_votea = 0  if M001249 == 6 & M001726 == 3
{txt}(4 real changes made)

{com}.                 label var d3_votea "D4 Vote Agreement"
{txt}
{com}.                 label values d3_votea agr
{txt}
{com}. 
. 
.                 gen d4_votea = . 
{txt}(1807 missing values generated)

{com}.                 replace d4_votea = 1 if M001249 == 1 & M001734 == 1
{txt}(70 real changes made)

{com}.                 replace d4_votea = 1 if M001249 == 3 & M001734 == 3
{txt}(93 real changes made)

{com}.                 replace d4_votea = 0  if M001249 == 1 & M001734 == 3
{txt}(41 real changes made)

{com}.                 replace d4_votea = 0  if M001249 == 3 & M001734 == 1
{txt}(24 real changes made)

{com}.                 replace d4_votea = 0  if M001249 == 1 & M001734 == 5
{txt}(1 real change made)

{com}.                 replace d4_votea = 0  if M001249 == 3 & M001734 == 5
{txt}(2 real changes made)

{com}.                 replace d4_votea = 0  if M001249 == 2 & M001734 == 1
{txt}(0 real changes made)

{com}.                 replace d4_votea = 0  if M001249 == 2 & M001734 == 3
{txt}(0 real changes made)

{com}.                 replace d4_votea = 0  if M001249 == 4 & M001734 == 1
{txt}(0 real changes made)

{com}.                 replace d4_votea = 0  if M001249 == 4 & M001734 == 3
{txt}(0 real changes made)

{com}.                 replace d4_votea = 0  if M001249 == 5 & M001734 == 1
{txt}(0 real changes made)

{com}.                 replace d4_votea = 0  if M001249 == 5 & M001734 == 3
{txt}(0 real changes made)

{com}.                 replace d4_votea = 0  if M001249 == 6 & M001734 == 1
{txt}(2 real changes made)

{com}.                 replace d4_votea = 0  if M001249 == 6 & M001734 == 3
{txt}(0 real changes made)

{com}.                 label var d4_votea "D4 Vote Agreement"
{txt}
{com}.                 label values d4_votea agr
{txt}
{com}.         
.                 tab d1_votea 

    {txt}D1 Vote {c |}
  Agreement {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
   Disagree {c |}{res}        214       26.03       26.03
{txt}      Agree {c |}{res}        608       73.97      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        822      100.00
{txt}
{com}.                 tab d2_votea 

    {txt}D2 Vote {c |}
  Agreement {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
   Disagree {c |}{res}        171       27.67       27.67
{txt}      Agree {c |}{res}        447       72.33      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        618      100.00
{txt}
{com}.                 tab d3_votea

    {txt}D4 Vote {c |}
  Agreement {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
   Disagree {c |}{res}        114       28.50       28.50
{txt}      Agree {c |}{res}        286       71.50      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        400      100.00
{txt}
{com}.                 tab d4_votea

    {txt}D4 Vote {c |}
  Agreement {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
   Disagree {c |}{res}         70       30.04       30.04
{txt}      Agree {c |}{res}        163       69.96      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        233      100.00
{txt}
{com}.                 
.         *Disagreement (=1; agree =0)
.                 label def disagr 1 "Disagree" 0 "Agree"
{txt}
{com}.                                         
.                 gen d1_voted = . 
{txt}(1807 missing values generated)

{com}.                 replace d1_voted = 0 if M001249 == 1 & M001710 == 1
{txt}(301 real changes made)

{com}.                 replace d1_voted = 0 if M001249 == 3 & M001710 == 3
{txt}(307 real changes made)

{com}.                 replace d1_voted = 1  if M001249 == 1 & M001710 == 3
{txt}(92 real changes made)

{com}.                 replace d1_voted = 1  if M001249 == 3 & M001710 == 1
{txt}(77 real changes made)

{com}.                 replace d1_voted = 1  if M001249 == 1 & M001710 == 5
{txt}(11 real changes made)

{com}.                 replace d1_voted = 1  if M001249 == 3 & M001710 == 5
{txt}(9 real changes made)

{com}.                 replace d1_voted = 1  if M001249 == 2 & M001710 == 1
{txt}(0 real changes made)

{com}.                 replace d1_voted = 1  if M001249 == 2 & M001710 == 3
{txt}(1 real change made)

{com}.                 replace d1_voted = 1  if M001249 == 4 & M001710 == 1
{txt}(2 real changes made)

{com}.                 replace d1_voted = 1  if M001249 == 4 & M001710 == 3
{txt}(1 real change made)

{com}.                 replace d1_voted = 1  if M001249 == 5 & M001710 == 1
{txt}(0 real changes made)

{com}.                 replace d1_voted = 1  if M001249 == 5 & M001710 == 3
{txt}(0 real changes made)

{com}.                 replace d1_voted = 1  if M001249 == 6 & M001710 == 1
{txt}(10 real changes made)

{com}.                 replace d1_voted = 1  if M001249 == 6 & M001710 == 3
{txt}(11 real changes made)

{com}.                 label var d1_voted "D1 Vote Disagreement"
{txt}
{com}.                 label values d1_voted disagr
{txt}
{com}.                 
.                 gen d2_voted = . 
{txt}(1807 missing values generated)

{com}.                 replace d2_voted = 0 if M001249 == 1 & M001718 == 1
{txt}(226 real changes made)

{com}.                 replace d2_voted = 0 if M001249 == 3 & M001718 == 3
{txt}(221 real changes made)

{com}.                 replace d2_voted = 1  if M001249 == 1 & M001718 == 3
{txt}(73 real changes made)

{com}.                 replace d2_voted = 1  if M001249 == 3 & M001718 == 1
{txt}(62 real changes made)

{com}.                 replace d2_voted = 1  if M001249 == 1 & M001718 == 5
{txt}(10 real changes made)

{com}.                 replace d2_voted = 1  if M001249 == 3 & M001718 == 5
{txt}(8 real changes made)

{com}.                 replace d2_voted = 1  if M001249 == 2 & M001718 == 1
{txt}(0 real changes made)

{com}.                 replace d2_voted = 1  if M001249 == 2 & M001718 == 3
{txt}(0 real changes made)

{com}.                 replace d2_voted = 1  if M001249 == 4 & M001718 == 1
{txt}(2 real changes made)

{com}.                 replace d2_voted = 1  if M001249 == 4 & M001718 == 3
{txt}(0 real changes made)

{com}.                 replace d2_voted = 1  if M001249 == 5 & M001718 == 1
{txt}(1 real change made)

{com}.                 replace d2_voted = 1  if M001249 == 5 & M001718 == 3
{txt}(0 real changes made)

{com}.                 replace d2_voted = 1  if M001249 == 6 & M001718 == 1
{txt}(10 real changes made)

{com}.                 replace d2_voted = 1  if M001249 == 6 & M001718 == 3
{txt}(5 real changes made)

{com}.                 label var d2_voted "D2 Vote Disagreement"
{txt}
{com}.                 label values d2_voted disagr
{txt}
{com}.                 
.                 gen d3_voted = . 
{txt}(1807 missing values generated)

{com}.                 replace d3_voted = 0 if M001249 == 1 & M001726 == 1
{txt}(137 real changes made)

{com}.                 replace d3_voted = 0 if M001249 == 3 & M001726 == 3
{txt}(149 real changes made)

{com}.                 replace d3_voted = 1  if M001249 == 1 & M001726 == 3
{txt}(53 real changes made)

{com}.                 replace d3_voted = 1  if M001249 == 3 & M001726 == 1
{txt}(43 real changes made)

{com}.                 replace d3_voted = 1  if M001249 == 1 & M001726 == 5
{txt}(3 real changes made)

{com}.                 replace d3_voted = 1  if M001249 == 3 & M001726 == 5
{txt}(4 real changes made)

{com}.                 replace d3_voted = 1  if M001249 == 2 & M001726 == 1
{txt}(0 real changes made)

{com}.                 replace d3_voted = 1  if M001249 == 2 & M001726 == 3
{txt}(1 real change made)

{com}.                 replace d3_voted = 1  if M001249 == 4 & M001726 == 1
{txt}(1 real change made)

{com}.                 replace d3_voted = 1  if M001249 == 4 & M001726 == 3
{txt}(1 real change made)

{com}.                 replace d3_voted = 1  if M001249 == 5 & M001726 == 1
{txt}(0 real changes made)

{com}.                 replace d3_voted = 1  if M001249 == 5 & M001726 == 3
{txt}(0 real changes made)

{com}.                 replace d3_voted = 1  if M001249 == 6 & M001726 == 1
{txt}(4 real changes made)

{com}.                 replace d3_voted = 1  if M001249 == 6 & M001726 == 3
{txt}(4 real changes made)

{com}.                 label var d3_voted "D3 Vote Disagreement"
{txt}
{com}.                 label values d3_voted disagr
{txt}
{com}.                 
.                 gen d4_voted = . 
{txt}(1807 missing values generated)

{com}.                 replace d4_voted = 0 if M001249 == 1 & M001734 == 1
{txt}(70 real changes made)

{com}.                 replace d4_voted = 0 if M001249 == 3 & M001734 == 3
{txt}(93 real changes made)

{com}.                 replace d4_voted = 1  if M001249 == 1 & M001734 == 3
{txt}(41 real changes made)

{com}.                 replace d4_voted = 1  if M001249 == 3 & M001734 == 1
{txt}(24 real changes made)

{com}.                 replace d4_voted = 1  if M001249 == 1 & M001734 == 5
{txt}(1 real change made)

{com}.                 replace d4_voted = 1  if M001249 == 3 & M001734 == 5
{txt}(2 real changes made)

{com}.                 replace d4_voted = 1  if M001249 == 2 & M001734 == 1
{txt}(0 real changes made)

{com}.                 replace d4_voted = 1  if M001249 == 2 & M001734 == 3
{txt}(0 real changes made)

{com}.                 replace d4_voted = 1  if M001249 == 4 & M001734 == 1
{txt}(0 real changes made)

{com}.                 replace d4_voted = 1  if M001249 == 4 & M001734 == 3
{txt}(0 real changes made)

{com}.                 replace d4_voted = 1  if M001249 == 5 & M001734 == 1
{txt}(0 real changes made)

{com}.                 replace d4_voted = 1  if M001249 == 5 & M001734 == 3
{txt}(0 real changes made)

{com}.                 replace d4_voted = 1  if M001249 == 6 & M001734 == 1
{txt}(2 real changes made)

{com}.                 replace d4_voted = 1  if M001249 == 6 & M001734 == 3
{txt}(0 real changes made)

{com}.                 label var d4_voted "D4 Vote Disagreement"
{txt}
{com}.                 label values d4_voted disagr
{txt}
{com}.                 
.                 tab d1_voted

    {txt}D1 Vote {c |}
Disagreemen {c |}
          t {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
      Agree {c |}{res}        608       73.97       73.97
{txt}   Disagree {c |}{res}        214       26.03      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        822      100.00
{txt}
{com}.                 tab d2_voted 

    {txt}D2 Vote {c |}
Disagreemen {c |}
          t {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
      Agree {c |}{res}        447       72.33       72.33
{txt}   Disagree {c |}{res}        171       27.67      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        618      100.00
{txt}
{com}.                 tab d3_voted

    {txt}D3 Vote {c |}
Disagreemen {c |}
          t {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
      Agree {c |}{res}        286       71.50       71.50
{txt}   Disagree {c |}{res}        114       28.50      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        400      100.00
{txt}
{com}.                 tab d4_voted

    {txt}D4 Vote {c |}
Disagreemen {c |}
          t {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
      Agree {c |}{res}        163       69.96       69.96
{txt}   Disagree {c |}{res}         70       30.04      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        233      100.00
{txt}
{com}. 
.                 
. *Summary and Average*
.         *Agreement*
.                 egen cand_agree = rowtotal(d1_votea d2_votea d3_votea d4_votea), missing
{txt}(917 missing values generated)

{com}.         *Disagreeement                          
.                 egen cand_disagree = rowtotal(d1_voted d2_voted d3_voted d4_voted), missing
{txt}(917 missing values generated)

{com}.                 
.                 tab cand_agree

 {txt}cand_agree {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        117       13.15       13.15
{txt}          1 {c |}{res}        297       33.37       46.52
{txt}          2 {c |}{res}        286       32.13       78.65
{txt}          3 {c |}{res}        125       14.04       92.70
{txt}          4 {c |}{res}         65        7.30      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        890      100.00
{txt}
{com}.                 tab cand_disagree

{txt}cand_disagr {c |}
         ee {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        496       55.73       55.73
{txt}          1 {c |}{res}        259       29.10       84.83
{txt}          2 {c |}{res}         99       11.12       95.96
{txt}          3 {c |}{res}         32        3.60       99.55
{txt}          4 {c |}{res}          4        0.45      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        890      100.00
{txt}
{com}.                 summ cand_agree cand_disagree

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 2}cand_agree {c |}{res}       890    1.689888    1.094217          0          4
{txt}cand_disag~e {c |}{res}       890    .6393258    .8506093          0          4
{txt}
{com}.                 
.         *Difference*
.                 *Summary Scale
.                         *See Lupton and Thornton: Exposure = D - A
.                                 gen disagree_total = cand_disagree - cand_agree
{txt}(917 missing values generated)

{com}.                                 label var disagree_total "Network Disagreement"
{txt}
{com}.                 *Corrected for # D & A
.                         *See Lupton & Thornton: (D-A)/(D+A)
.                                 gen disagree_avg = [cand_disagree - cand_agree] / [cand_disagree + cand_agree]
{txt}(917 missing values generated)

{com}.                                 label var disagree_avg "Network Disagreement"
{txt}
{com}.         
.                         *Standardized*
.                         foreach var in disagree_total disagree_avg {c -(}
{txt}  2{com}.                                 summ `var'
{txt}  3{com}.                                 gen `var'01 = (`var' - r(min))/(r(max)-r(min))
{txt}  4{com}.                         {c )-}

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
disagree_t~l {c |}{res}       890   -1.050562    1.631635         -4          4
{txt}(917 missing values generated)

    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
disagree_avg {c |}{res}       890    -.452809    .7176042         -1          1
{txt}(917 missing values generated)

{com}.                         
.                         label var disagree_total01 "Network Disagreement"
{txt}
{com}.                         label var disagree_avg01 "Network Disagreement"
{txt}
{com}.                                 
. *Network Diversity
.         *From Nir: [(Agree+Disagree)/2] - |A-D|
.                 gen network_ambiv = [(cand_agree + cand_disagree)/2] - abs(cand_agree - cand_disagree)
{txt}(917 missing values generated)

{com}.                 label var network_ambiv "Network Political Diversity"
{txt}
{com}.                 
.                 summ network_ambiv

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
network_am~v {c |}{res}       890   -.4477528    .9678874         -2          2
{txt}
{com}.                 gen network_ambiv01=(network_ambiv - r(min))/(r(max)-r(min))
{txt}(917 missing values generated)

{com}.                 label var network_ambiv01 "Network Political Diversity"
{txt}
{com}. 
.                         
.                 
.                 
.                 ***************************************************
.                 *****************Control Variables*****************
.                 ***************************************************
.                 
. *Education
.         recode M023131 (1=1) (2=1) (3=2) (4=3) (5=3) (6=4) (7=4) (9=.), gen(educ)
{txt}(1156 differences between M023131 and educ)

{com}.         tab M023131 educ

                      {txt}{c |}     RECODE of M023131 (Y3x. Summary: R
      Y3x. Summary: R {c |}                 Education)
            Education {c |}         1          2          3          4 {c |}     Total
{hline 22}{c +}{hline 44}{c +}{hline 10}
1. 8 grades or less a {c |}{res}        29          0          0          0 {txt}{c |}{res}        29 
{txt}2. 9-11 grades, no fu {c |}{res}        60          0          0          0 {txt}{c |}{res}        60 
{txt}3. High school diplom {c |}{res}         0        316          0          0 {txt}{c |}{res}       316 
{txt}4. More than 12 years {c |}{res}         0          0        245          0 {txt}{c |}{res}       245 
{txt}5. Junior or communit {c |}{res}         0          0        115          0 {txt}{c |}{res}       115 
{txt}6. BA level degrees;  {c |}{res}         0          0          0        273 {txt}{c |}{res}       273 
{txt}7. Advanced degree, i {c |}{res}         0          0          0        147 {txt}{c |}{res}       147 
{txt}{hline 22}{c +}{hline 44}{c +}{hline 10}
                Total {c |}{res}        89        316        360        420 {txt}{c |}{res}     1,185 

{txt}
{com}.         label var educ "Education"
{txt}
{com}.         label def edu 1 "< HS" 2 "HS" 3 "Some College" 4 "College Degree+"
{txt}
{com}.         label values educ edu
{txt}
{com}.         tab educ

      {txt}Education {c |}      Freq.     Percent        Cum.
{hline 16}{c +}{hline 35}
           < HS {c |}{res}         89        7.51        7.51
{txt}             HS {c |}{res}        316       26.67       34.18
{txt}   Some College {c |}{res}        360       30.38       64.56
{txt}College Degree+ {c |}{res}        420       35.44      100.00
{txt}{hline 16}{c +}{hline 35}
          Total {c |}{res}      1,185      100.00
{txt}
{com}.         
.         summ educ 

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 8}educ {c |}{res}      1185    2.937553    .9583289          1          4
{txt}
{com}.         gen educ01 = (educ - r(min))/(r(max)-r(min))
{txt}(622 missing values generated)

{com}.         label var educ01 "Education"
{txt}
{com}. 
. *Gender
.         recode  M023153 (1=0) (2=1), gen(gender)
{txt}(1187 differences between M023153 and gender)

{com}.         label var gender "Gender" 
{txt}
{com}.         label def gena 1 "Female" 0 "Male"
{txt}
{com}.         label values gender gena
{txt}
{com}. 
. 
. *Age
.         rename M023126X age2002
{res}{txt}
{com}.         label var age2002 "Age (2002)"
{txt}
{com}.         
.         summ age2002

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 5}age2002 {c |}{res}      1181      50.663    16.12556         20         99
{txt}
{com}.         gen age01 = (age2002 - r(min))/(r(max)-r(min))
{txt}(626 missing values generated)

{com}.         label var age01 "Age"
{txt}
{com}. 
. *Marital Status
.         recode  M023127A (1=1) (2=0) (3=0) (4=0) (5=0) (6=0), gen(marital)
{txt}(510 differences between M023127A and marital)

{com}.         label var marital "Marriage Status"
{txt}
{com}.         label def mar 1 "Married" 0 "Unmarried" 
{txt}
{com}.         label values marital mar
{txt}
{com}.         
.         recode M000909 (1=1) (2=0) (3=0) (4=0) (5=0) (6=0), gen(marital2000)
{txt}(858 differences between M000909 and marital2000)

{com}.         label var marital2000 "Marriage Status (2000)"
{txt}
{com}.         label values marital2000 mar
{txt}
{com}.         
.         
. 
. *Employment
.         recode M023132X (1=1) (2=2) (3=2) (4=3) (5=4) (6=5) (7=6) ///
>                 (14=1) (16=1) (17=1) ///
>                 (26=5) (34=3) (35=4) (36=5) ///
>                 (45=3) (46=5) (47=6) ///
>                 (67=6) (167=1) (467=4), gen(employment)
{txt}(425 differences between M023132X and employment)

{com}.                 label def emp 1 "Employed" 2 "Unemployed" 3 "Retired" 4 "Perm. Disabled" 5 "Homemaker" 6 "student" 
{txt}
{com}.                 label values employment emp
{txt}
{com}.                 tab employment

     {txt}RECODE of {c |}
M023132X (Y4x. {c |}
      Pre/Post {c |}
    Employment {c |}
        Status {c |}
      Summary) {c |}      Freq.     Percent        Cum.
{hline 15}{c +}{hline 35}
      Employed {c |}{res}        668       63.98       63.98
{txt}    Unemployed {c |}{res}         36        3.45       67.43
{txt}       Retired {c |}{res}        235       22.51       89.94
{txt}Perm. Disabled {c |}{res}         15        1.44       91.38
{txt}     Homemaker {c |}{res}         73        6.99       98.37
{txt}       student {c |}{res}         17        1.63      100.00
{txt}{hline 15}{c +}{hline 35}
         Total {c |}{res}      1,044      100.00
{txt}
{com}.                 
.         recode employment (1=1) (2=2) (3=3) (4=4) (5=4) (6=4), gen(employed)
{txt}(90 differences between employment and employed)

{com}.         label var employed "Employment Status"
{txt}
{com}.         label def emp1 1 "Employed" 2 "Unemployed" 3 "Retired" 4 "Perm. Disabled/Homemaker/Student"
{txt}
{com}.         label values employed emp1
{txt}
{com}.                 
. 
. *Inconme
.         recode  M023149 (9=.), gen(income2002)
{txt}(8 differences between M023149 and income2002)

{com}.         label var income2002 "Household Income (2002)"
{txt}
{com}.         
.         summ income2002

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 2}income2002 {c |}{res}      1143    4.234471    2.161428          1          8
{txt}
{com}.         gen income01 = (income2002 - r(min))/(r(max)-r(min))
{txt}(664 missing values generated)

{com}.         label var income01 "Household Income"
{txt}
{com}.         
.         
. *Race
.         label def rac 1 "White" 2 "Black" 3 "Hispanic" 4 "Other" 5 "Bi-Racial"
{txt}
{com}.         recode  M023150 (1=2) (2=4) (3=4) (4=3) (5=1) (12=5) (13=5) (14=3) (15=5) (24=3) (25=5) (34=3) (35=5) (45=3) (77=4), gen(race1)
{txt}(1180 differences between M023150 and race1)

{com}.         label var race1 "Race"
{txt}
{com}.         label values race1 rac
{txt}
{com}.         tab race

       {txt}Race {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
      White {c |}{res}        932       78.98       78.98
{txt}      Black {c |}{res}        104        8.81       87.80
{txt}   Hispanic {c |}{res}         77        6.53       94.32
{txt}      Other {c |}{res}         45        3.81       98.14
{txt}  Bi-Racial {c |}{res}         22        1.86      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,180      100.00
{txt}
{com}. 
.         recode race1 (1=1) (2=2) (3=3) (4=4) (5=4), gen(race)
{txt}(22 differences between race1 and race)

{com}.         label var race "Race"
{txt}
{com}.         label def rac1 1 "White" 2 "Black" 3 "Hispanic" 4 "Other" 
{txt}
{com}.         label values race rac1
{txt}
{com}.         
. *Personal Financial Situation
.         recode M023026 (1=5) (2=4) (3=3) (4=2) (5=1), gen(finance)
{txt}(571 differences between M023026 and finance)

{com}.         label def fin 1 "Much Worse" 2 "Somewhat worse" 3 "Same" 4 "Somewhat Better" 5 "Much Better" 
{txt}
{com}.         label values finance fin
{txt}
{com}.         tab finance

      {txt}RECODE of {c |}
M023026 (G1a. R {c |}
       How Much {c |}
Bett/Wrs Off in {c |}
       Last Yr) {c |}      Freq.     Percent        Cum.
{hline 16}{c +}{hline 35}
     Much Worse {c |}{res}         97        8.24        8.24
{txt} Somewhat worse {c |}{res}        235       19.97       28.21
{txt}           Same {c |}{res}        606       51.49       79.69
{txt}Somewhat Better {c |}{res}        171       14.53       94.22
{txt}    Much Better {c |}{res}         68        5.78      100.00
{txt}{hline 16}{c +}{hline 35}
          Total {c |}{res}      1,177      100.00
{txt}
{com}. 
. 
. *Interest*
.         recode M023001 (1=3) (3=2) (5=1), gen(interest_camp)
{txt}(1183 differences between M023001 and interest_camp)

{com}.         label var interest_camp "Interest in Campaign (2002)"
{txt}
{com}.         label def int 1 "Not Much" 2 "Somewhat" 3 "Very" 
{txt}
{com}.         label values interest_camp int
{txt}
{com}.         tab interest_camp

{txt}Interest in {c |}
   Campaign {c |}
     (2002) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
   Not Much {c |}{res}        242       20.46       20.46
{txt}   Somewhat {c |}{res}        663       56.04       76.50
{txt}       Very {c |}{res}        278       23.50      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,183      100.00
{txt}
{com}.         
.         summ interest_camp

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
interest_c~p {c |}{res}      1183    2.030431    .6625749          1          3
{txt}
{com}.         gen interest_camp01 = (interest_camp - r(min))/(r(max)-r(min))
{txt}(624 missing values generated)

{com}.         label var interest_camp "Campaign Interest (2002)"
{txt}
{com}.         
.         
. *Approval of Bush job on economy
.         recode  M045006x (1=4) (2=3) (4=2) (5=1), gen(bush1)
{txt}(828 differences between M045006x and bush1)

{com}.         recode  M023042X (1=4) (2=3) (4=2) (5=1), gen(bush2)
{txt}(1125 differences between M023042X and bush2)

{com}.         gen bush_econ = .
{txt}(1807 missing values generated)

{com}.         replace bush_econ = bush1
{txt}(828 real changes made)

{com}.         replace bush_econ = bush2 if bush_econ == .
{txt}(339 real changes made)

{com}.         label def becon 1 "Dis. Strongly" 2 "Dis. Not Strongly" 3 "App. Not Strongly" 4 "Appr. Strongly"
{txt}
{com}.         label values bush_econ becon
{txt}
{com}.         tab bush_econ

        {txt}bush_econ {c |}      Freq.     Percent        Cum.
{hline 18}{c +}{hline 35}
    Dis. Strongly {c |}{res}        410       35.13       35.13
{txt}Dis. Not Strongly {c |}{res}        152       13.02       48.16
{txt}App. Not Strongly {c |}{res}        238       20.39       68.55
{txt}   Appr. Strongly {c |}{res}        367       31.45      100.00
{txt}{hline 18}{c +}{hline 35}
            Total {c |}{res}      1,167      100.00
{txt}
{com}. 
. 
. *which party better able to handle the economy
.         recode M023031 (1=1) (3=2) (5=3) (7=3), gen(party_econ)
{txt}(812 differences between M023031 and party_econ)

{com}.         label var party_econ "Which Party Better at Economy?"
{txt}
{com}.         label def pat 1 "Democrats" 2 "Republicans" 3 "No Diff/Neither"
{txt}
{com}.         label values party_econ pat
{txt}
{com}.         tab party_econ

    {txt}Which Party {c |}
      Better at {c |}
       Economy? {c |}      Freq.     Percent        Cum.
{hline 16}{c +}{hline 35}
      Democrats {c |}{res}        349       30.06       30.06
{txt}    Republicans {c |}{res}        323       27.82       57.88
{txt}No Diff/Neither {c |}{res}        489       42.12      100.00
{txt}{hline 16}{c +}{hline 35}
          Total {c |}{res}      1,161      100.00
{txt}
{com}. 
. *Network Expertise
. 
.         foreach var in M001709 M001717 M001725 M001733 {c -(} 
{txt}  2{com}.                         tab `var'
{txt}  3{com}.                         {c )-}

    {txt}Z11. How much does name 1 {c |}
               know about pol {c |}      Freq.     Percent        Cum.
{hline 30}{c +}{hline 35}
              1. A GREAT DEAL {c |}{res}        483       42.22       42.22
{txt}         3. AN AVERAGE AMOUNT {c |}{res}        557       48.69       90.91
{txt}           5. NOT MUCH AT ALL {c |}{res}        104        9.09      100.00
{txt}{hline 30}{c +}{hline 35}
                        Total {c |}{res}      1,144      100.00

    {txt}Z15. How much does name 2 {c |}
               know about pol {c |}      Freq.     Percent        Cum.
{hline 30}{c +}{hline 35}
              1. A GREAT DEAL {c |}{res}        285       33.26       33.26
{txt}     3. AN AVERAGE AMOUNT, OR {c |}{res}        478       55.78       89.03
{txt}           5. NOT MUCH AT ALL {c |}{res}         94       10.97      100.00
{txt}{hline 30}{c +}{hline 35}
                        Total {c |}{res}        857      100.00

    {txt}Z19. How much does name 3 {c |}
               know about pol {c |}      Freq.     Percent        Cum.
{hline 30}{c +}{hline 35}
              1. A GREAT DEAL {c |}{res}        187       34.12       34.12
{txt}     3. AN AVERAGE AMOUNT, OR {c |}{res}        292       53.28       87.41
{txt}           5. NOT MUCH AT ALL {c |}{res}         69       12.59      100.00
{txt}{hline 30}{c +}{hline 35}
                        Total {c |}{res}        548      100.00

    {txt}Z23. How much does name 4 {c |}
               know about pol {c |}      Freq.     Percent        Cum.
{hline 30}{c +}{hline 35}
              1. A GREAT DEAL {c |}{res}        108       33.03       33.03
{txt}     3. AN AVERAGE AMOUNT, OR {c |}{res}        177       54.13       87.16
{txt}           5. NOT MUCH AT ALL {c |}{res}         42       12.84      100.00
{txt}{hline 30}{c +}{hline 35}
                        Total {c |}{res}        327      100.00
{txt}
{com}.                         
. 
.                 recode M001709 (1=3) (3=2) (5=1), gen(disc1_knowl)
{txt}(1144 differences between M001709 and disc1_knowl)

{com}.                 recode M001717 (1=3) (3=2) (5=1), gen(disc2_knowl)
{txt}(857 differences between M001717 and disc2_knowl)

{com}.                 recode M001725 (1=3) (3=2) (5=1), gen(disc3_knowl)
{txt}(548 differences between M001725 and disc3_knowl)

{com}.                 recode M001733 (1=3) (3=2) (5=1), gen(disc4_knowl)
{txt}(327 differences between M001733 and disc4_knowl)

{com}.                 mvdecode disc1_knowl disc2_knowl disc3_knowl disc4_knowl, mv(0 = .a \ 8 = .b \ 9 = .c)
{txt}
{com}.                 label def diknowl 1 "Not Much" 2 "Avg. Amount" 3 "Great Deal"
{txt}
{com}.                 foreach var in disc1_knowl disc2_knowl disc3_knowl disc4_knowl {c -(}
{txt}  2{com}.                         label values `var' diknowl
{txt}  3{com}.                         tab `var'
{txt}  4{com}.                         {c )-}

  {txt}RECODE of {c |}
    M001709 {c |}
  (Z11. How {c |}
  much does {c |}
name 1 know {c |}
 about pol) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
   Not Much {c |}{res}        104        9.09        9.09
{txt}Avg. Amount {c |}{res}        557       48.69       57.78
{txt} Great Deal {c |}{res}        483       42.22      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,144      100.00

  {txt}RECODE of {c |}
    M001717 {c |}
  (Z15. How {c |}
  much does {c |}
name 2 know {c |}
 about pol) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
   Not Much {c |}{res}         94       10.97       10.97
{txt}Avg. Amount {c |}{res}        478       55.78       66.74
{txt} Great Deal {c |}{res}        285       33.26      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        857      100.00

  {txt}RECODE of {c |}
    M001725 {c |}
  (Z19. How {c |}
  much does {c |}
name 3 know {c |}
 about pol) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
   Not Much {c |}{res}         69       12.59       12.59
{txt}Avg. Amount {c |}{res}        292       53.28       65.88
{txt} Great Deal {c |}{res}        187       34.12      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        548      100.00

  {txt}RECODE of {c |}
    M001733 {c |}
  (Z23. How {c |}
  much does {c |}
name 4 know {c |}
 about pol) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
   Not Much {c |}{res}         42       12.84       12.84
{txt}Avg. Amount {c |}{res}        177       54.13       66.97
{txt} Great Deal {c |}{res}        108       33.03      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        327      100.00
{txt}
{com}.                         
.                 egen disc_knowl = rowmean(disc1_knowl disc2_knowl disc3_knowl disc4_knowl)
{txt}(660 missing values generated)

{com}.                 label var disc_knowl "Network Pol. Knowl."
{txt}
{com}.                 summ disc_knowl 

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 2}disc_knowl {c |}{res}      1147    2.257338    .5115385          1          3
{txt}
{com}.                 
.                 summ disc_knowl

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 2}disc_knowl {c |}{res}      1147    2.257338    .5115385          1          3
{txt}
{com}.                 gen disc_knowl01 = (disc_knowl - r(min))/(r(max)-r(min))
{txt}(660 missing values generated)

{com}.                 label var disc_knowl01 "Network Pol. Knowledge"
{txt}
{com}.                 
.                 *Disagreement Scale Weighted by Disc Knowl*
.                         *Agree/Weight
.                         gen a1k = d1_votea * disc1_knowl 
{txt}(987 missing values generated)

{com}.                         gen a2k = d2_votea * disc2_knowl 
{txt}(1189 missing values generated)

{com}.                         gen a3k = d3_votea * disc3_knowl 
{txt}(1407 missing values generated)

{com}.                         gen a4k = d4_votea * disc4_knowl 
{txt}(1574 missing values generated)

{com}.                         egen agree_knowl = rowtotal(a1k a2k a3k a4k), missing
{txt}(917 missing values generated)

{com}.                 
.                         *Disagree/Weight
.                         gen d1k = d1_voted * disc1_knowl 
{txt}(987 missing values generated)

{com}.                         gen d2k = d2_voted * disc2_knowl 
{txt}(1189 missing values generated)

{com}.                         gen d3k = d3_voted * disc3_knowl 
{txt}(1407 missing values generated)

{com}.                         gen d4k = d4_voted * disc4_knowl 
{txt}(1574 missing values generated)

{com}.                         egen dagree_knowl = rowtotal(d1k d2k d3k d4k), missing
{txt}(917 missing values generated)

{com}.                 
.                         *Scale
.                                 gen disagree_total_knowl = dagree_knowl - agree_knowl
{txt}(917 missing values generated)

{com}.                                 gen disagree_avg_knowl = disagree_total_knowl/(cand_disagree + cand_agree)
{txt}(917 missing values generated)

{com}.                                 label var disagree_total_knowl "Network Disagreement (Knowledge Weighted)"
{txt}
{com}.                                 label var disagree_avg_knowl "Network Disagreement (Knowledge Weighted)"
{txt}
{com}.                                 
.                                 foreach var in disagree_total_knowl disagree_avg_knowl {c -(}
{txt}  2{com}.                                         summ `var'
{txt}  3{com}.                                         gen `var'01 = (`var' - r(min))/(r(max)-r(min))
{txt}  4{com}.                                         {c )-}

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
disa~l_knowl {c |}{res}       890    -2.55618    3.974849        -12         12
{txt}(917 missing values generated)

    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
disagree_a~l {c |}{res}       890   -1.094382    1.717809         -3          3
{txt}(917 missing values generated)

{com}.                                 
.                                 label var disagree_total_knowl01 "Network Disagreement (Knowledge Weighted)"
{txt}
{com}.                                 label var disagree_avg_knowl01 "Network Disagreement (Knowledge Weighted)"
{txt}
{com}.                                 
.                 
. *Freq. of Discussion
.                 
.         *When you talk with [fill name 1], do you discuss political matters…often, sometimes, rarely, or never?*
.         *1 = often; 3 = sometimes 5 = rarely 7 = never
.                 foreach var in  M001708 M001716 M001724 M001732 {c -(}
{txt}  2{com}.                         tab `var'
{txt}  3{com}.                         {c )-}

{txt}Z10. How often does R discuss {c |}
                   politics w {c |}      Freq.     Percent        Cum.
{hline 30}{c +}{hline 35}
                     1. OFTEN {c |}{res}        354       30.84       30.84
{txt}                 3. SOMETIMES {c |}{res}        593       51.66       82.49
{txt}                    5. RARELY {c |}{res}        195       16.99       99.48
{txt}                     7. NEVER {c |}{res}          6        0.52      100.00
{txt}{hline 30}{c +}{hline 35}
                        Total {c |}{res}      1,148      100.00

{txt}Z14. How often does R discuss {c |}
                   politics w {c |}      Freq.     Percent        Cum.
{hline 30}{c +}{hline 35}
                     1. OFTEN {c |}{res}        186       21.63       21.63
{txt}                 3. SOMETIMES {c |}{res}        483       56.16       77.79
{txt}                    5. RARELY {c |}{res}        189       21.98       99.77
{txt}                     7. NEVER {c |}{res}          2        0.23      100.00
{txt}{hline 30}{c +}{hline 35}
                        Total {c |}{res}        860      100.00

{txt}Z18. How often does R discuss {c |}
                   politics w {c |}      Freq.     Percent        Cum.
{hline 30}{c +}{hline 35}
                     1. OFTEN {c |}{res}        104       18.91       18.91
{txt}                 3. SOMETIMES {c |}{res}        295       53.64       72.55
{txt}                5. RARELY, OR {c |}{res}        147       26.73       99.27
{txt}                     7. NEVER {c |}{res}          4        0.73      100.00
{txt}{hline 30}{c +}{hline 35}
                        Total {c |}{res}        550      100.00

{txt}Z22. How often does R discuss {c |}
                   politics w {c |}      Freq.     Percent        Cum.
{hline 30}{c +}{hline 35}
                     1. OFTEN {c |}{res}         58       17.74       17.74
{txt}                 3. SOMETIMES {c |}{res}        168       51.38       69.11
{txt}                5. RARELY, OR {c |}{res}         97       29.66       98.78
{txt}                     7. NEVER {c |}{res}          4        1.22      100.00
{txt}{hline 30}{c +}{hline 35}
                        Total {c |}{res}        327      100.00
{txt}
{com}.                 *only 4-6 people say 'never' - they are thus lumped in with 'rarely'
.                 recode M001708 (1=3) (3=2) (5=1) (7=1), gen(disc1_freq)
{txt}(1148 differences between M001708 and disc1_freq)

{com}.                 tab M001708 disc1_freq

                      {txt}{c |}   RECODE of M001708 (Z10. How
Z10. How often does R {c |} often does R discuss politics w)
   discuss politics w {c |}         1          2          3 {c |}     Total
{hline 22}{c +}{hline 33}{c +}{hline 10}
             1. OFTEN {c |}{res}         0          0        354 {txt}{c |}{res}       354 
{txt}         3. SOMETIMES {c |}{res}         0        593          0 {txt}{c |}{res}       593 
{txt}            5. RARELY {c |}{res}       195          0          0 {txt}{c |}{res}       195 
{txt}             7. NEVER {c |}{res}         6          0          0 {txt}{c |}{res}         6 
{txt}{hline 22}{c +}{hline 33}{c +}{hline 10}
                Total {c |}{res}       201        593        354 {txt}{c |}{res}     1,148 

{txt}
{com}.                 recode M001716 (1=3) (3=2) (5=1) (7=1), gen(disc2_freq)
{txt}(860 differences between M001716 and disc2_freq)

{com}.                 tab M001716 disc2_freq

                      {txt}{c |}   RECODE of M001716 (Z14. How
Z14. How often does R {c |} often does R discuss politics w)
   discuss politics w {c |}         1          2          3 {c |}     Total
{hline 22}{c +}{hline 33}{c +}{hline 10}
             1. OFTEN {c |}{res}         0          0        186 {txt}{c |}{res}       186 
{txt}         3. SOMETIMES {c |}{res}         0        483          0 {txt}{c |}{res}       483 
{txt}            5. RARELY {c |}{res}       189          0          0 {txt}{c |}{res}       189 
{txt}             7. NEVER {c |}{res}         2          0          0 {txt}{c |}{res}         2 
{txt}{hline 22}{c +}{hline 33}{c +}{hline 10}
                Total {c |}{res}       191        483        186 {txt}{c |}{res}       860 

{txt}
{com}.                 
.                 recode M001724 (1=3) (3=2) (5=1) (7=1), gen(disc3_freq)
{txt}(550 differences between M001724 and disc3_freq)

{com}.                 tab M001724 disc3_freq

                      {txt}{c |}   RECODE of M001724 (Z18. How
Z18. How often does R {c |} often does R discuss politics w)
   discuss politics w {c |}         1          2          3 {c |}     Total
{hline 22}{c +}{hline 33}{c +}{hline 10}
             1. OFTEN {c |}{res}         0          0        104 {txt}{c |}{res}       104 
{txt}         3. SOMETIMES {c |}{res}         0        295          0 {txt}{c |}{res}       295 
{txt}        5. RARELY, OR {c |}{res}       147          0          0 {txt}{c |}{res}       147 
{txt}             7. NEVER {c |}{res}         4          0          0 {txt}{c |}{res}         4 
{txt}{hline 22}{c +}{hline 33}{c +}{hline 10}
                Total {c |}{res}       151        295        104 {txt}{c |}{res}       550 

{txt}
{com}.                 
.                 recode M001732 (1=3) (3=2) (5=1) (7=1), gen(disc4_freq)
{txt}(327 differences between M001732 and disc4_freq)

{com}.                 tab M001732 disc4_freq

                      {txt}{c |}   RECODE of M001732 (Z22. How
Z22. How often does R {c |} often does R discuss politics w)
   discuss politics w {c |}         1          2          3 {c |}     Total
{hline 22}{c +}{hline 33}{c +}{hline 10}
             1. OFTEN {c |}{res}         0          0         58 {txt}{c |}{res}        58 
{txt}         3. SOMETIMES {c |}{res}         0        168          0 {txt}{c |}{res}       168 
{txt}        5. RARELY, OR {c |}{res}        97          0          0 {txt}{c |}{res}        97 
{txt}             7. NEVER {c |}{res}         4          0          0 {txt}{c |}{res}         4 
{txt}{hline 22}{c +}{hline 33}{c +}{hline 10}
                Total {c |}{res}       101        168         58 {txt}{c |}{res}       327 

{txt}
{com}.                 
.                 mvdecode disc1_freq disc2_freq disc3_freq disc4_freq, mv(0 = .a \ 8 = .b \ 9 = .c)
{txt}
{com}.                 
.                 label def freq 3 "Often" 2 "Sometimes" 1 "Rarely/Never"
{txt}
{com}.                         
.                 foreach var  in disc1_freq disc2_freq disc3_freq disc4_freq {c -(}
{txt}  2{com}.                         label values `var' freq
{txt}  3{com}.                         tab `var'
{txt}  4{com}.                         {c )-}

   {txt}RECODE of {c |}
     M001708 {c |}
   (Z10. How {c |}
often does R {c |}
     discuss {c |}
 politics w) {c |}      Freq.     Percent        Cum.
{hline 13}{c +}{hline 35}
Rarely/Never {c |}{res}        201       17.51       17.51
{txt}   Sometimes {c |}{res}        593       51.66       69.16
{txt}       Often {c |}{res}        354       30.84      100.00
{txt}{hline 13}{c +}{hline 35}
       Total {c |}{res}      1,148      100.00

   {txt}RECODE of {c |}
     M001716 {c |}
   (Z14. How {c |}
often does R {c |}
     discuss {c |}
 politics w) {c |}      Freq.     Percent        Cum.
{hline 13}{c +}{hline 35}
Rarely/Never {c |}{res}        191       22.21       22.21
{txt}   Sometimes {c |}{res}        483       56.16       78.37
{txt}       Often {c |}{res}        186       21.63      100.00
{txt}{hline 13}{c +}{hline 35}
       Total {c |}{res}        860      100.00

   {txt}RECODE of {c |}
     M001724 {c |}
   (Z18. How {c |}
often does R {c |}
     discuss {c |}
 politics w) {c |}      Freq.     Percent        Cum.
{hline 13}{c +}{hline 35}
Rarely/Never {c |}{res}        151       27.45       27.45
{txt}   Sometimes {c |}{res}        295       53.64       81.09
{txt}       Often {c |}{res}        104       18.91      100.00
{txt}{hline 13}{c +}{hline 35}
       Total {c |}{res}        550      100.00

   {txt}RECODE of {c |}
     M001732 {c |}
   (Z22. How {c |}
often does R {c |}
     discuss {c |}
 politics w) {c |}      Freq.     Percent        Cum.
{hline 13}{c +}{hline 35}
Rarely/Never {c |}{res}        101       30.89       30.89
{txt}   Sometimes {c |}{res}        168       51.38       82.26
{txt}       Often {c |}{res}         58       17.74      100.00
{txt}{hline 13}{c +}{hline 35}
       Total {c |}{res}        327      100.00
{txt}
{com}.                         
.                 egen disc_freq = rowmean(disc1_freq disc2_freq disc3_freq disc4_freq)
{txt}(659 missing values generated)

{com}.                 label var disc_freq "Avg. Freq of Political Discussion"
{txt}
{com}.                 
.                 summ disc_freq, detail

              {txt}Avg. Freq of Political Discussion
{hline 61}
      Percentiles      Smallest
 1%    {res}        1              1
{txt} 5%    {res}        1              1
{txt}10%    {res}        1              1       {txt}Obs         {res}       1148
{txt}25%    {res} 1.666667              1       {txt}Sum of Wgt. {res}       1148

{txt}50%    {res}        2                      {txt}Mean          {res} 2.021632
                        {txt}Largest       Std. Dev.     {res} .5758626
{txt}75%    {res}      2.5              3
{txt}90%    {res}        3              3       {txt}Variance      {res} .3316178
{txt}95%    {res}        3              3       {txt}Skewness      {res}-.0695684
{txt}99%    {res}        3              3       {txt}Kurtosis      {res} 2.444665
{txt}
{com}.                 
.                 *Weighted Scale of Exposure to Disagreement
.                 
.                         *Agree/Weight
.                         gen a1f = d1_votea * disc1_freq 
{txt}(985 missing values generated)

{com}.                         gen a2f = d2_votea * disc2_freq 
{txt}(1189 missing values generated)

{com}.                         gen a3f = d3_votea * disc3_freq 
{txt}(1407 missing values generated)

{com}.                         gen a4f = d4_votea * disc4_freq 
{txt}(1574 missing values generated)

{com}.                         egen agree_freq = rowtotal(a1f a2f a3f a4f), missing
{txt}(917 missing values generated)

{com}.                 
.                         *Disagree/Weight
.                         gen d1f = d1_voted * disc1_freq 
{txt}(985 missing values generated)

{com}.                         gen d2f = d2_voted * disc2_freq 
{txt}(1189 missing values generated)

{com}.                         gen d3f = d3_voted * disc3_freq 
{txt}(1407 missing values generated)

{com}.                         gen d4f = d4_voted * disc4_freq 
{txt}(1574 missing values generated)

{com}.                         egen dagree_freq = rowtotal(d1f d2f d3f d4f), missing
{txt}(917 missing values generated)

{com}.         
.                 *Scale
.                                 gen disagree_total_freq = dagree_freq - agree_freq
{txt}(917 missing values generated)

{com}.                                 gen disagree_avg_freq = disagree_total_freq/(cand_disagree + cand_agree)
{txt}(917 missing values generated)

{com}.                                 label var disagree_total_freq "Network Disagreement (Frequency Weighted)"
{txt}
{com}.                                 label var disagree_avg_freq "Network Disagreement (Frequency Weighted)"
{txt}
{com}.                                 
.                                 foreach var in disagree_total_freq disagree_avg_freq {c -(}
{txt}  2{com}.                                         summ `var'
{txt}  3{com}.                                         gen `var'01 = (`var' - r(min))/(r(max)-r(min))
{txt}  4{com}.                                         {c )-}

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
disagree_t~q {c |}{res}       890   -2.392135    3.617565        -12         10
{txt}(917 missing values generated)

    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
disagree_a~q {c |}{res}       890   -1.022753    1.574888         -3          3
{txt}(917 missing values generated)

{com}.                                 
.                                 label var disagree_total_freq01 "Network Disagreement (Frequency Weighted)"
{txt}
{com}.                                 label var disagree_avg_freq01 "Network Disagreement (Frequency Weighted)"
{txt}
{com}.                                 
.                 
.                 
. *Follow Politics (2000)*
. recode  M001367 (1=4) (2=3) (3=2) (4=1), gen(follow)
{txt}(1543 differences between M001367 and follow)

{com}. label var follow "Political Interest"
{txt}
{com}. 
. summ follow 

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 6}follow {c |}{res}      1543    2.670771    .9672832          1          4
{txt}
{com}. gen follow01 = (follow - r(min))/(r(max)-r(min))
{txt}(264 missing values generated)

{com}. label var follow01 "Political Interest"
{txt}
{com}. 
. 
. 
. *Knowledge (2000)*
.         
. label def corre 1 "Correct" 0 "Incorrect/No Guess"
{txt}
{com}.         *lott 
.                 recode M001447 (1=1) (5=0), gen(lott)
{txt}(427 differences between M001447 and lott)

{com}.                 label var lott "Trent Lott Knowledge"
{txt}
{com}.                 label values lott corre
{txt}
{com}.         *reinquist
.                 recode M001450 (1=1) (5=0), gen(rein)
{txt}(486 differences between M001450 and rein)

{com}.                 label var rein "Reinquist Knowledge" 
{txt}
{com}.                 label values rein corre
{txt}
{com}.         *Blair
.                 recode M001453 (1=1) (5=0), gen(blair)
{txt}(142 differences between M001453 and blair)

{com}.                 label var blair "Blair Knowledge"
{txt}
{com}.                 label values blair corre
{txt}
{com}.         *Reno
.                 recode M001456 (1=1) (5=0), gen(reno)
{txt}(273 differences between M001456 and reno)

{com}.                 label var reno "Reno Knowledge"
{txt}
{com}.                 label values reno corre
{txt}
{com}.                 
.                 
.         *bush state: K3a
.                 recode M001458 (0=.) (1=0) (2=0) (3=1) (4=0) (7=0) , gen(bush_state)
{txt}(1449 differences between M001458 and bush_state)

{com}.                 label var bush_state "Bush State Knowledge"
{txt}
{com}.                 label values bush_state corre
{txt}
{com}.         *bush religion: K3b
.                 *correct = methodist*
.                 recode  M001460 (0=0) (1=0) (2=1) (3=0) (7=0), gen(bush_religion)
{txt}(987 differences between M001460 and bush_religion)

{com}.                 label var bush_religion "Bush Religion Knowledge"
{txt}
{com}.                 label values bush_religion corre
{txt}
{com}.         *gore state k4a
.                 *correct = tennessee
.                 recode M001462 (1=0) (2=1) (3=0) (4=0) (7=0) , gen(gore_state)
{txt}(1283 differences between M001462 and gore_state)

{com}.                 label var gore_state "Gore State Knowledge"
{txt}
{com}.                 label values gore_state corre
{txt}
{com}.         *gore religion: k4b
.                 *correct = baptist
.                 recode M001464 (0=0) (1=1) (2=0) (3=0) (7=0), gen(gore_religion)
{txt}(687 differences between M001464 and gore_religion)

{com}.                 label var gore_religion "Gore Religion Knowledge"
{txt}
{com}.                 label values gore_religion corre
{txt}
{com}.         *cheney state
.                 *correct = wyoming
.                 recode M001466 (1=0) (2=0) (3=0) (4=1) (7=0), gen(cheney_state)
{txt}(731 differences between M001466 and cheney_state)

{com}.                 label var cheney_state "Cheney State Knowledge"
{txt}
{com}.                 label values cheney_state corre
{txt}
{com}.         *cheney religion
.                 *correct = methodist*
.                 recode M001468 (0=0) (1=0) (2=1) (3=0) (7=0), gen(cheney_religion)
{txt}(639 differences between M001468 and cheney_religion)

{com}.                 label var cheney_religion "Cheney Religion Knowledge"
{txt}
{com}.                 label values cheney_religion corre
{txt}
{com}.         *lieberman state
.                 *correct = ct
.                 recode M001470  (1=1) (2=0) (3=0) (4=0) (7=0) , gen(lieb_state)
{txt}(387 differences between M001470 and lieb_state)

{com}.                 label var lieb_state "Liberman State Knowledge"
{txt}
{com}.                 label values lieb_state corre
{txt}
{com}.         *lieberman religion
.                 *correct = jewish
.                 recode M001472 (0=0) (1=0) (2=0) (3=1) (7=0) , gen(lieb_religion)
{txt}(1194 differences between M001472 and lieb_religion)

{com}.                 label var lieb_religion "Liberman Religion Knowledge"
{txt}
{com}.                 label values lieb_religion corre
{txt}
{com}. 
.                         
.                         egen knowl = rowtotal(lott rein blair reno bush_state bush_religion gore_state gore_religion cheney_state cheney_religion lieb_religion lieb_state), missing
{txt}(286 missing values generated)

{com}.         label var knowl "Knowledge"
{txt}
{com}.         tab knowl

  {txt}Knowledge {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}         64        4.21        4.21
{txt}          1 {c |}{res}        159       10.45       14.66
{txt}          2 {c |}{res}        185       12.16       26.82
{txt}          3 {c |}{res}        223       14.66       41.49
{txt}          4 {c |}{res}        225       14.79       56.28
{txt}          5 {c |}{res}        203       13.35       69.63
{txt}          6 {c |}{res}        182       11.97       81.59
{txt}          7 {c |}{res}        123        8.09       89.68
{txt}          8 {c |}{res}         77        5.06       94.74
{txt}          9 {c |}{res}         64        4.21       98.95
{txt}         10 {c |}{res}         13        0.85       99.80
{txt}         11 {c |}{res}          3        0.20      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,521      100.00
{txt}
{com}.         summ knowl, detail

                          {txt}Knowledge
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        1              0
{txt}10%    {res}        1              0       {txt}Obs         {res}       1521
{txt}25%    {res}        2              0       {txt}Sum of Wgt. {res}       1521

{txt}50%    {res}        4                      {txt}Mean          {res} 4.221565
                        {txt}Largest       Std. Dev.     {res} 2.410003
{txt}75%    {res}        6             10
{txt}90%    {res}        8             11       {txt}Variance      {res} 5.808114
{txt}95%    {res}        9             11       {txt}Skewness      {res} .2712036
{txt}99%    {res}       10             11       {txt}Kurtosis      {res} 2.372471
{txt}
{com}.         
.         summ knowl

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 7}knowl {c |}{res}      1521    4.221565    2.410003          0         11
{txt}
{com}.         gen knowl01 = (knowl - r(min))/(r(max)-r(min))
{txt}(286 missing values generated)

{com}.         label var knowl01 "Knowledge"
{txt}
{com}. 
.         
. *Ideology (2000)*
. tab  M000446

      {txt}G6x1. Summary self plcmnt lib-con {c |}
                                 scale/ {c |}      Freq.     Percent        Cum.
{hline 40}{c +}{hline 35}
1. SCALE: 1 / BRANCHING: strong liberal {c |}{res}         82        5.05        5.05
{txt}2. SCALE: 2 / BRANCHING: not strong lib {c |}{res}        158        9.74       14.79
{txt}3. SCALE: 3. had to choose liberal / BR {c |}{res}        351       21.63       36.41
{txt}4. SCALE: 4. had to choose moderate/ BR {c |}{res}        109        6.72       43.13
{txt}5. SCALE: 5. had to choose conserv/ BRA {c |}{res}        528       32.53       75.66
{txt}6. SCALE: 6 / BRANCHING: not strong con {c |}{res}        250       15.40       91.07
{txt}7. SCALE: 7 / BRANCHING: strong conserv {c |}{res}        145        8.93      100.00
{txt}{hline 40}{c +}{hline 35}
                                  Total {c |}{res}      1,623      100.00
{txt}
{com}.         rename M000446 ideology
{res}{txt}
{com}.         mvdecode ideology, mv(0 = .a \ 8 = .b \ 9 = .c)
{txt}
{com}.         tab ideology

      {txt}G6x1. Summary self plcmnt lib-con {c |}
                                 scale/ {c |}      Freq.     Percent        Cum.
{hline 40}{c +}{hline 35}
1. SCALE: 1 / BRANCHING: strong liberal {c |}{res}         82        5.05        5.05
{txt}2. SCALE: 2 / BRANCHING: not strong lib {c |}{res}        158        9.74       14.79
{txt}3. SCALE: 3. had to choose liberal / BR {c |}{res}        351       21.63       36.41
{txt}4. SCALE: 4. had to choose moderate/ BR {c |}{res}        109        6.72       43.13
{txt}5. SCALE: 5. had to choose conserv/ BRA {c |}{res}        528       32.53       75.66
{txt}6. SCALE: 6 / BRANCHING: not strong con {c |}{res}        250       15.40       91.07
{txt}7. SCALE: 7 / BRANCHING: strong conserv {c |}{res}        145        8.93      100.00
{txt}{hline 40}{c +}{hline 35}
                                  Total {c |}{res}      1,623      100.00
{txt}
{com}.         
.         summ ideology

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 4}ideology {c |}{res}      1623    4.338879    1.640956          1          7
{txt}
{com}.         gen ideology01 = (ideology - r(min))/(r(max)-r(min))
{txt}(184 missing values generated)

{com}.         label var ideology01 "Ideology"
{txt}
{com}.         
.         recode ideology (1=4) (2=3) (3=2) (4=1) (5=2) (6=3) (7=4), gen(ideol_str)
{txt}(1623 differences between ideology and ideol_str)

{com}.         label var ideol_str "Ideological Extremity"
{txt}
{com}.         
.         summ ideol_str

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 3}ideol_str {c |}{res}      1623    2.463956    .8143448          1          4
{txt}
{com}.         gen ideol_str01 = (ideol_str - r(min))/(r(max)-r(min))
{txt}(184 missing values generated)

{com}.         label var ideol_str01 "Ideological Extremity"
{txt}
{com}. 
. 
.         
.         
. **Candidate Ambivalence (pre-election)
. 
. gen gore_likes = . 
{txt}(1807 missing values generated)

{com}. replace gore_likes = 0 if M000305 == 5
{txt}(878 real changes made)

{com}. replace gore_likes = 1 if M000305 == 1 & M000306 !=. & M000307 == .
{txt}(295 real changes made)

{com}. replace gore_likes = 2 if M000305 == 1 & M000306 !=. & M000307 !=. & M000308 ==. 
{txt}(220 real changes made)

{com}. replace gore_likes = 3 if M000305 == 1 & M000306 !=. & M000307 !=. & M000308 !=. & M000309 ==. 
{txt}(168 real changes made)

{com}. replace gore_likes = 4 if M000305 == 1 & M000306 !=. & M000307 !=. & M000308 !=. & M000309 !=. & M000310 ==.
{txt}(104 real changes made)

{com}. replace gore_likes = 5 if M000305 == 1 &  M000306 !=. & M000307 !=. & M000308 !=. & M000309 !=.  & M000310 !=.
{txt}(115 real changes made)

{com}. 
. gen gore_dislikes = .
{txt}(1807 missing values generated)

{com}. replace gore_dislikes = 0 if M000311 == 5
{txt}(934 real changes made)

{com}. 
. replace gore_dislikes = 1 if M000311 == 1 & M000312 !=. & M000313 == .
{txt}(316 real changes made)

{com}. replace gore_dislikes = 2 if M000311 == 1 & M000312 !=. & M000313 !=. & M000314 ==. 
{txt}(243 real changes made)

{com}. replace gore_dislikes = 3 if M000311 == 1 & M000312 !=. & M000313 !=. & M000314 !=. & M000315 ==. 
{txt}(147 real changes made)

{com}. replace gore_dislikes = 4 if M000311 == 1 & M000312 !=. & M000313 !=. & M000314 !=. & M000315 !=. & M000316 ==.
{txt}(73 real changes made)

{com}. replace gore_dislikes = 5 if M000311 == 1 &  M000312 !=. & M000313 !=. & M000314 !=. & M000315 !=.  & M000316 !=.
{txt}(65 real changes made)

{com}. 
. 
. 
. gen bush_likes = .
{txt}(1807 missing values generated)

{com}. replace bush_likes = 0 if M000317 == 5
{txt}(970 real changes made)

{com}. replace bush_likes = 1 if M000317 == 1 & M000318 !=. & M000319 == .
{txt}(272 real changes made)

{com}. replace bush_likes = 2 if M000317 == 1 & M000318 !=. & M000319 !=. & M000320 ==. 
{txt}(237 real changes made)

{com}. replace bush_likes = 3 if M000317 == 1 & M000318 !=. & M000319 !=. & M000320 !=. & M000321 ==. 
{txt}(135 real changes made)

{com}. replace bush_likes = 4 if M000317 == 1 & M000318 !=. & M000319 !=. & M000320 !=. & M000321 !=. & M000322 ==.
{txt}(75 real changes made)

{com}. replace bush_likes = 5 if M000317 == 1 &  M000318 !=. & M000319 !=. & M000320 !=. & M000321 !=.  & M000322 !=.
{txt}(94 real changes made)

{com}. 
. 
. 
. 
. gen bush_dislikes = .
{txt}(1807 missing values generated)

{com}. replace bush_dislikes = 0 if M000323 == 5
{txt}(899 real changes made)

{com}. replace bush_dislikes = 1 if M000323 == 1 & M000324 !=. & M000325 == .
{txt}(324 real changes made)

{com}. replace bush_dislikes = 2 if M000323 == 1 & M000324 !=. & M000325 !=. & M000326 ==. 
{txt}(236 real changes made)

{com}. replace bush_dislikes = 3 if M000323 == 1 & M000324 !=. & M000325 !=. & M000326 !=. & M000327 ==. 
{txt}(150 real changes made)

{com}. replace bush_dislikes = 4 if M000323 == 1 & M000324 !=. & M000325 !=. & M000326 !=. & M000327 !=. & M000328 ==.
{txt}(78 real changes made)

{com}. replace bush_dislikes = 5 if M000323 == 1 &  M000324 !=. & M000325 !=. & M000326 !=. & M000327 !=.  & M000328 !=.
{txt}(88 real changes made)

{com}. 
. 
.                         
. *Campaign Interest and Caring About Election - 2000
.         
. recode  M000301 (1=3) (3=2) (5=1), gen(camp_int2000)
{txt}(1807 differences between M000301 and camp_int2000)

{com}. label var camp_int2000 "Campaign Interest"
{txt}
{com}. 
. recode M000302 (1=1) (3=0) (8=0), gen(cares2000)
{txt}(397 differences between M000302 and cares2000)

{com}. label var cares2000 "Cares about Election"
{txt}
{com}. 
. 
. 
. gen dem_likes = . 
{txt}(1807 missing values generated)

{com}. replace dem_likes = 0 if M000373 == 5
{txt}(766 real changes made)

{com}. replace dem_likes = 1 if M000373 == 1 & M000374 !=. & M000375 == .
{txt}(390 real changes made)

{com}. replace dem_likes = 2 if M000373 == 1 & M000374 !=. & M000375 !=. & M000376 ==. 
{txt}(276 real changes made)

{com}. replace dem_likes = 3 if M000373 == 1 & M000374 !=. & M000375 !=. & M000376 !=. & M000377 ==. 
{txt}(175 real changes made)

{com}. replace dem_likes = 4 if M000373 == 1 & M000374 !=. & M000375 !=. & M000376 !=. & M000377 !=. & M000378 ==.
{txt}(95 real changes made)

{com}. replace dem_likes = 5 if M000373 == 1 &  M000374 !=. & M000375 !=. & M000376 !=. & M000377 !=.  & M000378 !=.
{txt}(77 real changes made)

{com}. 
. 
. 
. gen dem_dislikes = .
{txt}(1807 missing values generated)

{com}. replace dem_dislikes = 0 if M000379 == 5
{txt}(996 real changes made)

{com}. replace dem_dislikes = 1 if M000379 == 1 & M000380 !=. & M000381 == .
{txt}(324 real changes made)

{com}. replace dem_dislikes = 2 if M000379 == 1 & M000380 !=. & M000381 !=. & M000382 ==. 
{txt}(228 real changes made)

{com}. replace dem_dislikes = 3 if M000379 == 1 & M000380 !=. & M000381 !=. & M000382 !=. & M000383 ==. 
{txt}(121 real changes made)

{com}. replace dem_dislikes = 4 if M000379 == 1 & M000380 !=. & M000381 !=. & M000382 !=. & M000383 !=. & M000384 ==.
{txt}(54 real changes made)

{com}. replace dem_dislikes = 5 if M000379 == 1 &  M000380 !=. & M000381 !=. & M000382 !=. & M000383 !=.  & M000384 !=.
{txt}(49 real changes made)

{com}. 
. 
. 
. gen rep_likes = .
{txt}(1807 missing values generated)

{com}. replace rep_likes = 0 if M000385 == 5
{txt}(975 real changes made)

{com}. replace rep_likes = 1 if M000385 == 1 & M000386 !=. & M000387 == .
{txt}(303 real changes made)

{com}. replace rep_likes = 2 if M000385 == 1 & M000386 !=. & M000387 !=. & M000388 ==. 
{txt}(224 real changes made)

{com}. replace rep_likes = 3 if M000385 == 1 & M000386 !=. & M000387 !=. & M000388 !=. & M000389 ==. 
{txt}(135 real changes made)

{com}. replace rep_likes = 4 if M000385 == 1 & M000386 !=. & M000387 !=. & M000388 !=. & M000389 !=. & M000390 ==.
{txt}(78 real changes made)

{com}. replace rep_likes = 5 if M000385 == 1 &  M000386 !=. & M000387 !=. & M000388 !=. & M000389 !=.  & M000390 !=.
{txt}(59 real changes made)

{com}. 
. 
. gen rep_dislikes = .
{txt}(1807 missing values generated)

{com}. replace rep_dislikes = 0 if M000391 == 5
{txt}(867 real changes made)

{com}. replace rep_dislikes = 1 if M000391 == 1 & M000392 !=. & M000393 == .
{txt}(355 real changes made)

{com}. replace rep_dislikes = 2 if M000391 == 1 & M000392 !=. & M000393 !=. & M000394 ==. 
{txt}(276 real changes made)

{com}. replace rep_dislikes = 3 if M000391 == 1 & M000392 !=. & M000393 !=. & M000394 !=. & M000327 ==. 
{txt}(199 real changes made)

{com}. replace rep_dislikes = 4 if M000391 == 1 & M000392 !=. & M000393 !=. & M000394 !=. & M000327 !=. & M000396 ==.
{txt}(45 real changes made)

{com}. replace rep_dislikes = 5 if M000391 == 1 &  M000392 !=. & M000393 !=. & M000394 !=. & M000327 !=.  & M000396 !=.
{txt}(33 real changes made)

{com}. 
. 
.         *ID Consistent
.                 gen consistent = . 
{txt}(1807 missing values generated)

{com}.                 replace consistent = dem_likes + rep_dislikes if pid_2_2000  == 1
{txt}(867 real changes made)

{com}.                 replace consistent = dem_dislikes + rep_likes if pid_2_2000  == 0
{txt}(673 real changes made)

{com}.                 label var consistent "Partisan Identity Consistent Likes/Dislikes"
{txt}
{com}. 
.         *ID Conflicting
.                 gen conflicting = . 
{txt}(1807 missing values generated)

{com}.                 replace conflicting = dem_dislikes + rep_likes if pid_2_2000   == 1
{txt}(869 real changes made)

{com}.                 replace conflicting = dem_likes + rep_dislikes if pid_2_2000   == 0
{txt}(678 real changes made)

{com}.                 label var conflicting "Partisan Identity Conclifting Likes/Dislikes"
{txt}
{com}. 
. *for matching analyses
. recode M000917 (1=1) (2=1) (3=2) (4=3) (5=3) (6=4) (7=4) (9=.), gen(educ2000)
{txt}(947 differences between M000917 and educ2000)

{com}.         tab educ2000 M000917

 {txt}RECODE of {c |}
   M000917 {c |}
  (Y4x. Sp {c |}
     educ. {c |}                            Y4x. Sp educ. Summary
  Summary) {c |} 1. 8 grad  2. 9-11 g  3. High s  4. More t  5. Junior  6. BA lev  7. Advanc {c |}     Total
{hline 11}{c +}{hline 77}{c +}{hline 10}
         1 {c |}{res}        29         56          0          0          0          0          0 {txt}{c |}{res}        85 
{txt}         2 {c |}{res}         0          0        297          0          0          0          0 {txt}{c |}{res}       297 
{txt}         3 {c |}{res}         0          0          0        174         85          0          0 {txt}{c |}{res}       259 
{txt}         4 {c |}{res}         0          0          0          0          0        218        117 {txt}{c |}{res}       335 
{txt}{hline 11}{c +}{hline 77}{c +}{hline 10}
     Total {c |}{res}        29         56        297        174         85        218        117 {txt}{c |}{res}       976 

{txt}
{com}.         label var educ2000 "Education"
{txt}
{com}.         label def edua 1 "< HS" 2 "HS" 3 "Some College" 4 "College Degree+"
{txt}
{com}.         label values educ2000 edua
{txt}
{com}.         tab educ2000

      {txt}Education {c |}      Freq.     Percent        Cum.
{hline 16}{c +}{hline 35}
           < HS {c |}{res}         85        8.71        8.71
{txt}             HS {c |}{res}        297       30.43       39.14
{txt}   Some College {c |}{res}        259       26.54       65.68
{txt}College Degree+ {c |}{res}        335       34.32      100.00
{txt}{hline 16}{c +}{hline 35}
          Total {c |}{res}        976      100.00
{txt}
{com}.         
. rename M000997 income2000
{res}{txt}
{com}. label var income2000 "Income"
{txt}
{com}. rename M000908 age2000
{res}{txt}
{com}. label var age2000 "Age"
{txt}
{com}. 
. 
. tabulate camp_int2000, gen(cint_)

   {txt}Campaign {c |}
   Interest {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}        396       21.91       21.91
{txt}          2 {c |}{res}        886       49.03       70.95
{txt}          3 {c |}{res}        525       29.05      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,807      100.00
{txt}
{com}. tabulate pid_str2000, gen(pstr_)

   {txt}PID Str. {c |}
     (2000) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
     Leaner {c |}{res}        499       31.60       31.60
{txt}       Weak {c |}{res}        489       30.97       62.57
{txt}     Strong {c |}{res}        591       37.43      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,579      100.00
{txt}
{com}. 
. 
. 
. *****Need for cognition & need to evaluate****
. 
. 
. 
. recode M000862 (0=.) (8=.) (1=4) (2=3) (3=2) (4=1) (9=.), gen(opinionated)
{txt}(1798 differences between M000862 and opinionated)

{com}. recode M000866 (0 8 9 = .) , gen(opinion_degree)
{txt}(0 differences between M000866 and opinion_degree)

{com}. 
. foreach var in opinionated opinion_degree {c -(}
{txt}  2{com}.         summ `var'
{txt}  3{com}.         gen `var'01 = (`var' - r(min))/(r(max)-r(min))
{txt}  4{com}.         {c )-}

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 1}opinionated {c |}{res}      1798    2.670189      .86399          1          4
{txt}(9 missing values generated)

    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
opinion_de~e {c |}{res}      1771    3.288538    .9488492          1          5
{txt}(36 missing values generated)

{com}. 
. egen evaluate1 = rowtotal(opinionated01 opinion_degree01), missing
{txt}(7 missing values generated)

{com}. 
.         
. recode M000871 (0 8 9 =.) (1=0) (5=1) , gen(complex)
{txt}(1761 differences between M000871 and complex)

{com}. recode M000870 (0 8 = .) (5=0) (4=0.25) (3=0.50) (2=0.75) (1=1), gen(thinking)
{txt}(1088 differences between M000870 and thinking)

{com}. 
. egen nfc1 = rowtotal(complex thinking), missing
{txt}(5 missing values generated)

{com}.         
. 
{txt}end of do-file

{com}. 
. 
. 
. /***Partisan Extremity****/
. eststo clear
{txt}
{com}. foreach var in disagree_total disagree_avg disagree_total_freq disagree_total_knowl  {c -(}
{txt}  2{com}.         eststo:  ologit pid_str_full c.`var' names1 disc_knowl i.pid_2_2000 follow  ///
>                         i.gender i.race age2000 educ2000 income2000  i.marital2000 evaluate1 nfc1 [pweight=WT02PRE]
{txt}  3{com}.                 {c )-}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:  -398.198}  
Iteration 1:{space 3}log pseudolikelihood = {res:-385.61458}  
Iteration 2:{space 3}log pseudolikelihood = {res:-385.55269}  
Iteration 3:{space 3}log pseudolikelihood = {res:-385.55267}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       360
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     24.66
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0547
{txt}Log pseudolikelihood = {res}-385.55267{txt}{col 51}Pseudo R2{col 67}= {res}    0.0318

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}  pid_str_full{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
disagree_total {c |}{col 16}{res}{space 2}-.1280732{col 28}{space 2} .0773583{col 39}{space 1}   -1.66{col 48}{space 3}0.098{col 56}{space 4}-.2796927{col 69}{space 3} .0235463
{txt}{space 8}names1 {c |}{col 16}{res}{space 2} .0725514{col 28}{space 2} .1012007{col 39}{space 1}    0.72{col 48}{space 3}0.473{col 56}{space 4}-.1257984{col 69}{space 3} .2709012
{txt}{space 4}disc_knowl {c |}{col 16}{res}{space 2} .4595737{col 28}{space 2}  .260212{col 39}{space 1}    1.77{col 48}{space 3}0.077{col 56}{space 4}-.0504325{col 69}{space 3} .9695798
{txt}{space 14} {c |}
{space 4}pid_2_2000 {c |}
{space 5}Democrat  {c |}{col 16}{res}{space 2}-.5801991{col 28}{space 2} .2432385{col 39}{space 1}   -2.39{col 48}{space 3}0.017{col 56}{space 4}-1.056938{col 69}{space 3}-.1034605
{txt}{space 8}follow {c |}{col 16}{res}{space 2} .1103802{col 28}{space 2} .2058904{col 39}{space 1}    0.54{col 48}{space 3}0.592{col 56}{space 4}-.2931575{col 69}{space 3} .5139179
{txt}{space 14} {c |}
{space 8}gender {c |}
{space 7}Female  {c |}{col 16}{res}{space 2} .2580395{col 28}{space 2}  .262384{col 39}{space 1}    0.98{col 48}{space 3}0.325{col 56}{space 4}-.2562238{col 69}{space 3} .7723028
{txt}{space 14} {c |}
{space 10}race {c |}
{space 8}Black  {c |}{col 16}{res}{space 2} .7888969{col 28}{space 2} .7484484{col 39}{space 1}    1.05{col 48}{space 3}0.292{col 56}{space 4}-.6780351{col 69}{space 3} 2.255829
{txt}{space 5}Hispanic  {c |}{col 16}{res}{space 2}-.1325548{col 28}{space 2} .3922664{col 39}{space 1}   -0.34{col 48}{space 3}0.735{col 56}{space 4} -.901383{col 69}{space 3} .6362733
{txt}{space 8}Other  {c |}{col 16}{res}{space 2}-.4753217{col 28}{space 2} .6037219{col 39}{space 1}   -0.79{col 48}{space 3}0.431{col 56}{space 4}-1.658595{col 69}{space 3} .7079515
{txt}{space 14} {c |}
{space 7}age2000 {c |}{col 16}{res}{space 2} .0044916{col 28}{space 2} .0096532{col 39}{space 1}    0.47{col 48}{space 3}0.642{col 56}{space 4}-.0144283{col 69}{space 3} .0234114
{txt}{space 6}educ2000 {c |}{col 16}{res}{space 2} -.061367{col 28}{space 2} .1357502{col 39}{space 1}   -0.45{col 48}{space 3}0.651{col 56}{space 4}-.3274326{col 69}{space 3} .2046986
{txt}{space 4}income2000 {c |}{col 16}{res}{space 2}-.0045115{col 28}{space 2} .0361129{col 39}{space 1}   -0.12{col 48}{space 3}0.901{col 56}{space 4}-.0752914{col 69}{space 3} .0662684
{txt}{space 14} {c |}
{space 3}marital2000 {c |}
{space 6}Married  {c |}{col 16}{res}{space 2} .1867377{col 28}{space 2} .5838167{col 39}{space 1}    0.32{col 48}{space 3}0.749{col 56}{space 4}-.9575221{col 69}{space 3} 1.330997
{txt}{space 5}evaluate1 {c |}{col 16}{res}{space 2}-.5401659{col 28}{space 2} .3716129{col 39}{space 1}   -1.45{col 48}{space 3}0.146{col 56}{space 4}-1.268514{col 69}{space 3}  .188182
{txt}{space 10}nfc1 {c |}{col 16}{res}{space 2} .0178626{col 28}{space 2} .1837705{col 39}{space 1}    0.10{col 48}{space 3}0.923{col 56}{space 4} -.342321{col 69}{space 3} .3780462
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
         /cut1 {c |}{col 16}{res}{space 2}-2.055413{col 28}{space 2} .9403365{col 56}{space 4}-3.898438{col 69}{space 3}-.2123872
{txt}         /cut2 {c |}{col 16}{res}{space 2}  .231035{col 28}{space 2} .8898673{col 56}{space 4}-1.513073{col 69}{space 3} 1.975143
{txt}         /cut3 {c |}{col 16}{res}{space 2} 1.712023{col 28}{space 2} .8941566{col 56}{space 4}-.0404916{col 69}{space 3} 3.464538
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:  -398.198}  
Iteration 1:{space 3}log pseudolikelihood = {res:-385.19263}  
Iteration 2:{space 3}log pseudolikelihood = {res: -385.1199}  
Iteration 3:{space 3}log pseudolikelihood = {res:-385.11986}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       360
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     24.79
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0529
{txt}Log pseudolikelihood = {res}-385.11986{txt}{col 51}Pseudo R2{col 67}= {res}    0.0328

{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}pid_str_full{col 14}{c |}      Coef.{col 26}   Std. Err.{col 38}      z{col 46}   P>|z|{col 54}     [95% Con{col 67}f. Interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
disagree_avg {c |}{col 14}{res}{space 2}-.3380129{col 26}{space 2} .1800493{col 37}{space 1}   -1.88{col 46}{space 3}0.060{col 54}{space 4} -.690903{col 67}{space 3} .0148773
{txt}{space 6}names1 {c |}{col 14}{res}{space 2} .1277515{col 26}{space 2} .0966786{col 37}{space 1}    1.32{col 46}{space 3}0.186{col 54}{space 4}-.0617352{col 67}{space 3} .3172381
{txt}{space 2}disc_knowl {c |}{col 14}{res}{space 2} .4950155{col 26}{space 2} .2608348{col 37}{space 1}    1.90{col 46}{space 3}0.058{col 54}{space 4}-.0162114{col 67}{space 3} 1.006242
{txt}{space 12} {c |}
{space 2}pid_2_2000 {c |}
{space 3}Democrat  {c |}{col 14}{res}{space 2}-.5864486{col 26}{space 2} .2427136{col 37}{space 1}   -2.42{col 46}{space 3}0.016{col 54}{space 4}-1.062159{col 67}{space 3}-.1107386
{txt}{space 6}follow {c |}{col 14}{res}{space 2} .1078703{col 26}{space 2} .2031388{col 37}{space 1}    0.53{col 46}{space 3}0.595{col 54}{space 4}-.2902744{col 67}{space 3}  .506015
{txt}{space 12} {c |}
{space 6}gender {c |}
{space 5}Female  {c |}{col 14}{res}{space 2} .2368058{col 26}{space 2}  .262096{col 37}{space 1}    0.90{col 46}{space 3}0.366{col 54}{space 4}-.2768929{col 67}{space 3} .7505046
{txt}{space 12} {c |}
{space 8}race {c |}
{space 6}Black  {c |}{col 14}{res}{space 2} .7769803{col 26}{space 2} .7481284{col 37}{space 1}    1.04{col 46}{space 3}0.299{col 54}{space 4}-.6893245{col 67}{space 3} 2.243285
{txt}{space 3}Hispanic  {c |}{col 14}{res}{space 2} -.148165{col 26}{space 2} .3887803{col 37}{space 1}   -0.38{col 46}{space 3}0.703{col 54}{space 4}-.9101603{col 67}{space 3} .6138303
{txt}{space 6}Other  {c |}{col 14}{res}{space 2}-.4907263{col 26}{space 2} .6000568{col 37}{space 1}   -0.82{col 46}{space 3}0.413{col 54}{space 4}-1.666816{col 67}{space 3} .6853635
{txt}{space 12} {c |}
{space 5}age2000 {c |}{col 14}{res}{space 2} .0048888{col 26}{space 2} .0095804{col 37}{space 1}    0.51{col 46}{space 3}0.610{col 54}{space 4}-.0138885{col 67}{space 3} .0236661
{txt}{space 4}educ2000 {c |}{col 14}{res}{space 2}-.0563948{col 26}{space 2} .1356843{col 37}{space 1}   -0.42{col 46}{space 3}0.678{col 54}{space 4}-.3223311{col 67}{space 3} .2095416
{txt}{space 2}income2000 {c |}{col 14}{res}{space 2}-.0034289{col 26}{space 2} .0356216{col 37}{space 1}   -0.10{col 46}{space 3}0.923{col 54}{space 4} -.073246{col 67}{space 3} .0663882
{txt}{space 12} {c |}
{space 1}marital2000 {c |}
{space 4}Married  {c |}{col 14}{res}{space 2} .2032237{col 26}{space 2} .5836477{col 37}{space 1}    0.35{col 46}{space 3}0.728{col 54}{space 4}-.9407047{col 67}{space 3} 1.347152
{txt}{space 3}evaluate1 {c |}{col 14}{res}{space 2}-.5627422{col 26}{space 2} .3699883{col 37}{space 1}   -1.52{col 46}{space 3}0.128{col 54}{space 4}-1.287906{col 67}{space 3} .1624216
{txt}{space 8}nfc1 {c |}{col 14}{res}{space 2} .0222275{col 26}{space 2} .1843194{col 37}{space 1}    0.12{col 46}{space 3}0.904{col 54}{space 4} -.339032{col 67}{space 3}  .383487
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
       /cut1 {c |}{col 14}{res}{space 2}-1.805067{col 26}{space 2} .9460536{col 54}{space 4}-3.659298{col 67}{space 3} .0491639
{txt}       /cut2 {c |}{col 14}{res}{space 2} .4881423{col 26}{space 2} .8928083{col 54}{space 4} -1.26173{col 67}{space 3} 2.238014
{txt}       /cut3 {c |}{col 14}{res}{space 2} 1.972077{col 26}{space 2} .8965265{col 54}{space 4} .2149169{col 67}{space 3} 3.729236
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:  -398.198}  
Iteration 1:{space 3}log pseudolikelihood = {res:-384.98389}  
Iteration 2:{space 3}log pseudolikelihood = {res:  -384.917}  
Iteration 3:{space 3}log pseudolikelihood = {res:-384.91697}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       360
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     26.71
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0312
{txt}Log pseudolikelihood = {res}-384.91697{txt}{col 51}Pseudo R2{col 67}= {res}    0.0334

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}       pid_str_full{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
disagree_total_freq {c |}{col 21}{res}{space 2}-.0692398{col 33}{space 2} .0346201{col 44}{space 1}   -2.00{col 53}{space 3}0.046{col 61}{space 4} -.137094{col 74}{space 3}-.0013856
{txt}{space 13}names1 {c |}{col 21}{res}{space 2} .0611517{col 33}{space 2} .1019131{col 44}{space 1}    0.60{col 53}{space 3}0.548{col 61}{space 4}-.1385944{col 74}{space 3} .2608977
{txt}{space 9}disc_knowl {c |}{col 21}{res}{space 2} .4331661{col 33}{space 2}  .261644{col 44}{space 1}    1.66{col 53}{space 3}0.098{col 61}{space 4}-.0796467{col 74}{space 3} .9459788
{txt}{space 19} {c |}
{space 9}pid_2_2000 {c |}
{space 10}Democrat  {c |}{col 21}{res}{space 2}-.5618461{col 33}{space 2} .2434382{col 44}{space 1}   -2.31{col 53}{space 3}0.021{col 61}{space 4}-1.038976{col 74}{space 3}-.0847159
{txt}{space 13}follow {c |}{col 21}{res}{space 2}  .098284{col 33}{space 2} .2044043{col 44}{space 1}    0.48{col 53}{space 3}0.631{col 61}{space 4} -.302341{col 74}{space 3} .4989089
{txt}{space 19} {c |}
{space 13}gender {c |}
{space 12}Female  {c |}{col 21}{res}{space 2} .2474851{col 33}{space 2} .2632085{col 44}{space 1}    0.94{col 53}{space 3}0.347{col 61}{space 4}-.2683941{col 74}{space 3} .7633642
{txt}{space 19} {c |}
{space 15}race {c |}
{space 13}Black  {c |}{col 21}{res}{space 2} .7769249{col 33}{space 2} .7499648{col 44}{space 1}    1.04{col 53}{space 3}0.300{col 61}{space 4}-.6929791{col 74}{space 3} 2.246829
{txt}{space 10}Hispanic  {c |}{col 21}{res}{space 2}-.1510345{col 33}{space 2} .3859332{col 44}{space 1}   -0.39{col 53}{space 3}0.696{col 61}{space 4}-.9074496{col 74}{space 3} .6053805
{txt}{space 13}Other  {c |}{col 21}{res}{space 2}-.4745082{col 33}{space 2} .6043921{col 44}{space 1}   -0.79{col 53}{space 3}0.432{col 61}{space 4}-1.659095{col 74}{space 3} .7100785
{txt}{space 19} {c |}
{space 12}age2000 {c |}{col 21}{res}{space 2} .0033854{col 33}{space 2} .0096434{col 44}{space 1}    0.35{col 53}{space 3}0.726{col 61}{space 4}-.0155153{col 74}{space 3}  .022286
{txt}{space 11}educ2000 {c |}{col 21}{res}{space 2}-.0648713{col 33}{space 2} .1358433{col 44}{space 1}   -0.48{col 53}{space 3}0.633{col 61}{space 4}-.3311193{col 74}{space 3} .2013767
{txt}{space 9}income2000 {c |}{col 21}{res}{space 2}-.0022195{col 33}{space 2} .0360738{col 44}{space 1}   -0.06{col 53}{space 3}0.951{col 61}{space 4}-.0729229{col 74}{space 3} .0684839
{txt}{space 19} {c |}
{space 8}marital2000 {c |}
{space 11}Married  {c |}{col 21}{res}{space 2} .1825492{col 33}{space 2} .5767486{col 44}{space 1}    0.32{col 53}{space 3}0.752{col 61}{space 4}-.9478572{col 74}{space 3} 1.312956
{txt}{space 10}evaluate1 {c |}{col 21}{res}{space 2}-.5438262{col 33}{space 2}  .370762{col 44}{space 1}   -1.47{col 53}{space 3}0.142{col 61}{space 4}-1.270506{col 74}{space 3} .1828539
{txt}{space 15}nfc1 {c |}{col 21}{res}{space 2} .0059059{col 33}{space 2} .1855339{col 44}{space 1}    0.03{col 53}{space 3}0.975{col 61}{space 4} -.357734{col 74}{space 3} .3695457
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
              /cut1 {c |}{col 21}{res}{space 2}-2.226206{col 33}{space 2} .9532166{col 61}{space 4}-4.094476{col 74}{space 3}-.3579355
{txt}              /cut2 {c |}{col 21}{res}{space 2} .0639546{col 33}{space 2} .9043009{col 61}{space 4}-1.708443{col 74}{space 3} 1.836352
{txt}              /cut3 {c |}{col 21}{res}{space 2} 1.548469{col 33}{space 2} .9078845{col 61}{space 4} -.230952{col 74}{space 3}  3.32789
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:  -398.198}  
Iteration 1:{space 3}log pseudolikelihood = {res:-385.15242}  
Iteration 2:{space 3}log pseudolikelihood = {res:-385.08748}  
Iteration 3:{space 3}log pseudolikelihood = {res:-385.08745}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       360
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     25.10
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0486
{txt}Log pseudolikelihood = {res}-385.08745{txt}{col 51}Pseudo R2{col 67}= {res}    0.0329

{txt}{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}        pid_str_full{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
disagree_total_knowl {c |}{col 22}{res}{space 2}-.0606742{col 34}{space 2} .0313087{col 45}{space 1}   -1.94{col 54}{space 3}0.053{col 62}{space 4}-.1220381{col 75}{space 3} .0006897
{txt}{space 14}names1 {c |}{col 22}{res}{space 2}  .062621{col 34}{space 2} .1016907{col 45}{space 1}    0.62{col 54}{space 3}0.538{col 62}{space 4}-.1366891{col 75}{space 3} .2619311
{txt}{space 10}disc_knowl {c |}{col 22}{res}{space 2}  .393536{col 34}{space 2} .2675469{col 45}{space 1}    1.47{col 54}{space 3}0.141{col 62}{space 4}-.1308464{col 75}{space 3} .9179183
{txt}{space 20} {c |}
{space 10}pid_2_2000 {c |}
{space 11}Democrat  {c |}{col 22}{res}{space 2}-.5728448{col 34}{space 2} .2439803{col 45}{space 1}   -2.35{col 54}{space 3}0.019{col 62}{space 4}-1.051037{col 75}{space 3}-.0946522
{txt}{space 14}follow {c |}{col 22}{res}{space 2} .1140235{col 34}{space 2} .2071412{col 45}{space 1}    0.55{col 54}{space 3}0.582{col 62}{space 4}-.2919657{col 75}{space 3} .5200128
{txt}{space 20} {c |}
{space 14}gender {c |}
{space 13}Female  {c |}{col 22}{res}{space 2} .2507447{col 34}{space 2} .2625743{col 45}{space 1}    0.95{col 54}{space 3}0.340{col 62}{space 4}-.2638914{col 75}{space 3} .7653808
{txt}{space 20} {c |}
{space 16}race {c |}
{space 14}Black  {c |}{col 22}{res}{space 2} .7977643{col 34}{space 2} .7547953{col 45}{space 1}    1.06{col 54}{space 3}0.291{col 62}{space 4}-.6816073{col 75}{space 3} 2.277136
{txt}{space 11}Hispanic  {c |}{col 22}{res}{space 2} -.122228{col 34}{space 2} .3967361{col 45}{space 1}   -0.31{col 54}{space 3}0.758{col 62}{space 4}-.8998165{col 75}{space 3} .6553605
{txt}{space 14}Other  {c |}{col 22}{res}{space 2}-.4808189{col 34}{space 2} .5985925{col 45}{space 1}   -0.80{col 54}{space 3}0.422{col 62}{space 4}-1.654039{col 75}{space 3} .6924009
{txt}{space 20} {c |}
{space 13}age2000 {c |}{col 22}{res}{space 2} .0043492{col 34}{space 2} .0095735{col 45}{space 1}    0.45{col 54}{space 3}0.650{col 62}{space 4}-.0144146{col 75}{space 3} .0231129
{txt}{space 12}educ2000 {c |}{col 22}{res}{space 2}-.0643878{col 34}{space 2} .1365477{col 45}{space 1}   -0.47{col 54}{space 3}0.637{col 62}{space 4}-.3320164{col 75}{space 3} .2032408
{txt}{space 10}income2000 {c |}{col 22}{res}{space 2}-.0064995{col 34}{space 2} .0363793{col 45}{space 1}   -0.18{col 54}{space 3}0.858{col 62}{space 4}-.0778017{col 75}{space 3} .0648027
{txt}{space 20} {c |}
{space 9}marital2000 {c |}
{space 12}Married  {c |}{col 22}{res}{space 2}  .186982{col 34}{space 2} .5842667{col 45}{space 1}    0.32{col 54}{space 3}0.749{col 62}{space 4}-.9581597{col 75}{space 3} 1.332124
{txt}{space 11}evaluate1 {c |}{col 22}{res}{space 2}-.5357807{col 34}{space 2} .3720985{col 45}{space 1}   -1.44{col 54}{space 3}0.150{col 62}{space 4} -1.26508{col 75}{space 3}  .193519
{txt}{space 16}nfc1 {c |}{col 22}{res}{space 2} .0147774{col 34}{space 2} .1832356{col 45}{space 1}    0.08{col 54}{space 3}0.936{col 62}{space 4}-.3443577{col 75}{space 3} .3739126
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
               /cut1 {c |}{col 22}{res}{space 2}-2.229724{col 34}{space 2}  .954426{col 62}{space 4}-4.100364{col 75}{space 3} -.359083
{txt}               /cut2 {c |}{col 22}{res}{space 2} .0577138{col 34}{space 2} .9063666{col 62}{space 4}-1.718732{col 75}{space 3}  1.83416
{txt}               /cut3 {c |}{col 22}{res}{space 2} 1.541442{col 34}{space 2} .9096086{col 62}{space 4} -.241358{col 75}{space 3} 3.324242
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{com}.                 
. 
. esttab using 2002_ALTMEASURES_EXT.rtf, onecell nobaselevels replace label pr2 aic bic se star(+ 0.10 * 0.05 ** 0.01) ///
>         mtitles("Original Measure" "/(D+A)" "Weigh: Disc. Freq" "Weigh: Disc. Knowl")  ///
>         rename(disagree_total Disagreement disagree_avg Disagreement disagree_total_freq Disagreement ///
>                                 disagree_total_knowl Disagreement) 
{res}{txt}(output written to {browse  `"2002_ALTMEASURES_EXT.rtf"'})

{com}. eststo clear
{txt}
{com}. 
. /***Economic Evaluations***/
. eststo clear
{txt}
{com}. foreach var in disagree_total disagree_avg disagree_total_freq disagree_total_knowl  {c -(}
{txt}  2{com}.         eststo: ologit retro2002_3 i.partisan2000##c.`var' names1 disc_knowl ///
>         follow age2002 educ income2002 i.gender i.race i.employed i.marital nfc1 evaluate1 [pweight=WT02PRE]
{txt}  3{com}.         {c )-}       

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-465.09183}  
Iteration 1:{space 3}log pseudolikelihood = {res:-431.60631}  
Iteration 2:{space 3}log pseudolikelihood = {res:-431.18488}  
Iteration 3:{space 3}log pseudolikelihood = {res:-431.18367}  
Iteration 4:{space 3}log pseudolikelihood = {res:-431.18367}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       549
{txt}{col 51}Wald chi2({res}19{txt}){col 67}= {res}     51.82
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0001
{txt}Log pseudolikelihood = {res}-431.18367{txt}{col 51}Pseudo R2{col 67}= {res}    0.0729

{txt}{hline 34}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 35}{c |}{col 47}    Robust
{col 1}                      retro2002_3{col 35}{c |}      Coef.{col 47}   Std. Err.{col 59}      z{col 67}   P>|z|{col 75}     [95% Con{col 88}f. Interval]
{hline 34}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}partisan2000 {c |}
{space 21}In-Partisan  {c |}{col 35}{res}{space 2}-1.171401{col 47}{space 2} .2592493{col 58}{space 1}   -4.52{col 67}{space 3}0.000{col 75}{space 4} -1.67952{col 88}{space 3}-.6632816
{txt}{space 19}disagree_total {c |}{col 35}{res}{space 2}-.0039288{col 47}{space 2} .0804639{col 58}{space 1}   -0.05{col 67}{space 3}0.961{col 75}{space 4}-.1616351{col 88}{space 3} .1537775
{txt}{space 33} {c |}
{space 4}partisan2000#c.disagree_total {c |}
{space 21}In-Partisan  {c |}{col 35}{res}{space 2}-.0131786{col 47}{space 2}  .123048{col 58}{space 1}   -0.11{col 67}{space 3}0.915{col 75}{space 4}-.2543481{col 88}{space 3}  .227991
{txt}{space 33} {c |}
{space 27}names1 {c |}{col 35}{res}{space 2}-.2449951{col 47}{space 2} .0903066{col 58}{space 1}   -2.71{col 67}{space 3}0.007{col 75}{space 4}-.4219928{col 88}{space 3}-.0679973
{txt}{space 23}disc_knowl {c |}{col 35}{res}{space 2} .0793229{col 47}{space 2} .2106918{col 58}{space 1}    0.38{col 67}{space 3}0.707{col 75}{space 4}-.3336255{col 88}{space 3} .4922713
{txt}{space 27}follow {c |}{col 35}{res}{space 2} .0376786{col 47}{space 2} .1328755{col 58}{space 1}    0.28{col 67}{space 3}0.777{col 75}{space 4}-.2227527{col 88}{space 3} .2981099
{txt}{space 26}age2002 {c |}{col 35}{res}{space 2}-.0033681{col 47}{space 2}  .009047{col 58}{space 1}   -0.37{col 67}{space 3}0.710{col 75}{space 4}-.0210999{col 88}{space 3} .0143638
{txt}{space 29}educ {c |}{col 35}{res}{space 2}-.0768688{col 47}{space 2} .1324272{col 58}{space 1}   -0.58{col 67}{space 3}0.562{col 75}{space 4}-.3364213{col 88}{space 3} .1826838
{txt}{space 23}income2002 {c |}{col 35}{res}{space 2} .0337261{col 47}{space 2}  .063054{col 58}{space 1}    0.53{col 67}{space 3}0.593{col 75}{space 4}-.0898575{col 88}{space 3} .1573097
{txt}{space 33} {c |}
{space 27}gender {c |}
{space 26}Female  {c |}{col 35}{res}{space 2}-.2868988{col 47}{space 2}  .214217{col 58}{space 1}   -1.34{col 67}{space 3}0.180{col 75}{space 4}-.7067564{col 88}{space 3} .1329589
{txt}{space 33} {c |}
{space 29}race {c |}
{space 27}Black  {c |}{col 35}{res}{space 2} .1682316{col 47}{space 2} .4240471{col 58}{space 1}    0.40{col 67}{space 3}0.692{col 75}{space 4}-.6628854{col 88}{space 3} .9993486
{txt}{space 24}Hispanic  {c |}{col 35}{res}{space 2} .4618229{col 47}{space 2} .4370228{col 58}{space 1}    1.06{col 67}{space 3}0.291{col 75}{space 4} -.394726{col 88}{space 3} 1.318372
{txt}{space 27}Other  {c |}{col 35}{res}{space 2} .3018524{col 47}{space 2} .5331298{col 58}{space 1}    0.57{col 67}{space 3}0.571{col 75}{space 4}-.7430628{col 88}{space 3} 1.346768
{txt}{space 33} {c |}
{space 25}employed {c |}
{space 22}Unemployed  {c |}{col 35}{res}{space 2} .4412443{col 47}{space 2} .6204831{col 58}{space 1}    0.71{col 67}{space 3}0.477{col 75}{space 4}-.7748802{col 88}{space 3} 1.657369
{txt}{space 25}Retired  {c |}{col 35}{res}{space 2} .3163892{col 47}{space 2} .3505185{col 58}{space 1}    0.90{col 67}{space 3}0.367{col 75}{space 4}-.3706145{col 88}{space 3} 1.003393
{txt}Perm. Disabled/Homemaker/Student  {c |}{col 35}{res}{space 2} .9129926{col 47}{space 2} .3521855{col 58}{space 1}    2.59{col 67}{space 3}0.010{col 75}{space 4} .2227216{col 88}{space 3} 1.603264
{txt}{space 33} {c |}
{space 26}marital {c |}
{space 25}Married  {c |}{col 35}{res}{space 2}-.5074442{col 47}{space 2} .2396358{col 58}{space 1}   -2.12{col 67}{space 3}0.034{col 75}{space 4}-.9771218{col 88}{space 3}-.0377665
{txt}{space 29}nfc1 {c |}{col 35}{res}{space 2} .1058868{col 47}{space 2} .1643576{col 58}{space 1}    0.64{col 67}{space 3}0.519{col 75}{space 4}-.2162482{col 88}{space 3} .4280218
{txt}{space 24}evaluate1 {c |}{col 35}{res}{space 2} -.365457{col 47}{space 2} .2784155{col 58}{space 1}   -1.31{col 67}{space 3}0.189{col 75}{space 4}-.9111413{col 88}{space 3} .1802274
{txt}{hline 34}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                            /cut1 {c |}{col 35}{res}{space 2}-3.713983{col 47}{space 2}  .792616{col 75}{space 4}-5.267482{col 88}{space 3}-2.160484
{txt}                            /cut2 {c |}{col 35}{res}{space 2}-1.343777{col 47}{space 2} .7924044{col 75}{space 4}-2.896861{col 88}{space 3}  .209307
{txt}{hline 34}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-465.09183}  
Iteration 1:{space 3}log pseudolikelihood = {res:-431.19117}  
Iteration 2:{space 3}log pseudolikelihood = {res:-430.75936}  
Iteration 3:{space 3}log pseudolikelihood = {res:-430.75807}  
Iteration 4:{space 3}log pseudolikelihood = {res:-430.75807}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       549
{txt}{col 51}Wald chi2({res}19{txt}){col 67}= {res}     52.89
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-430.75807{txt}{col 51}Pseudo R2{col 67}= {res}    0.0738

{txt}{hline 34}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 35}{c |}{col 47}    Robust
{col 1}                      retro2002_3{col 35}{c |}      Coef.{col 47}   Std. Err.{col 59}      z{col 67}   P>|z|{col 75}     [95% Con{col 88}f. Interval]
{hline 34}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}partisan2000 {c |}
{space 21}In-Partisan  {c |}{col 35}{res}{space 2}-1.280221{col 47}{space 2} .2519913{col 58}{space 1}   -5.08{col 67}{space 3}0.000{col 75}{space 4}-1.774114{col 88}{space 3}-.7863268
{txt}{space 21}disagree_avg {c |}{col 35}{res}{space 2} .1066793{col 47}{space 2} .1909563{col 58}{space 1}    0.56{col 67}{space 3}0.576{col 75}{space 4}-.2675882{col 88}{space 3} .4809467
{txt}{space 33} {c |}
{space 6}partisan2000#c.disagree_avg {c |}
{space 21}In-Partisan  {c |}{col 35}{res}{space 2}-.2490363{col 47}{space 2} .2804498{col 58}{space 1}   -0.89{col 67}{space 3}0.375{col 75}{space 4}-.7987078{col 88}{space 3} .3006352
{txt}{space 33} {c |}
{space 27}names1 {c |}{col 35}{res}{space 2} -.242195{col 47}{space 2} .0877101{col 58}{space 1}   -2.76{col 67}{space 3}0.006{col 75}{space 4}-.4141036{col 88}{space 3}-.0702864
{txt}{space 23}disc_knowl {c |}{col 35}{res}{space 2} .0838667{col 47}{space 2} .2092684{col 58}{space 1}    0.40{col 67}{space 3}0.689{col 75}{space 4}-.3262918{col 88}{space 3} .4940252
{txt}{space 27}follow {c |}{col 35}{res}{space 2} .0423637{col 47}{space 2} .1343496{col 58}{space 1}    0.32{col 67}{space 3}0.753{col 75}{space 4}-.2209567{col 88}{space 3} .3056842
{txt}{space 26}age2002 {c |}{col 35}{res}{space 2}-.0034992{col 47}{space 2} .0090577{col 58}{space 1}   -0.39{col 67}{space 3}0.699{col 75}{space 4}-.0212519{col 88}{space 3} .0142536
{txt}{space 29}educ {c |}{col 35}{res}{space 2}-.0743516{col 47}{space 2} .1331347{col 58}{space 1}   -0.56{col 67}{space 3}0.577{col 75}{space 4}-.3352908{col 88}{space 3} .1865875
{txt}{space 23}income2002 {c |}{col 35}{res}{space 2} .0339409{col 47}{space 2} .0632352{col 58}{space 1}    0.54{col 67}{space 3}0.591{col 75}{space 4}-.0899979{col 88}{space 3} .1578797
{txt}{space 33} {c |}
{space 27}gender {c |}
{space 26}Female  {c |}{col 35}{res}{space 2}-.2769314{col 47}{space 2} .2150347{col 58}{space 1}   -1.29{col 67}{space 3}0.198{col 75}{space 4}-.6983917{col 88}{space 3} .1445289
{txt}{space 33} {c |}
{space 29}race {c |}
{space 27}Black  {c |}{col 35}{res}{space 2} .1742201{col 47}{space 2} .4259174{col 58}{space 1}    0.41{col 67}{space 3}0.683{col 75}{space 4}-.6605627{col 88}{space 3} 1.009003
{txt}{space 24}Hispanic  {c |}{col 35}{res}{space 2} .4606905{col 47}{space 2} .4397621{col 58}{space 1}    1.05{col 67}{space 3}0.295{col 75}{space 4}-.4012274{col 88}{space 3} 1.322608
{txt}{space 27}Other  {c |}{col 35}{res}{space 2} .3065836{col 47}{space 2} .5373935{col 58}{space 1}    0.57{col 67}{space 3}0.568{col 75}{space 4}-.7466882{col 88}{space 3} 1.359855
{txt}{space 33} {c |}
{space 25}employed {c |}
{space 22}Unemployed  {c |}{col 35}{res}{space 2} .4158635{col 47}{space 2} .6205358{col 58}{space 1}    0.67{col 67}{space 3}0.503{col 75}{space 4}-.8003643{col 88}{space 3} 1.632091
{txt}{space 25}Retired  {c |}{col 35}{res}{space 2} .3269157{col 47}{space 2} .3506265{col 58}{space 1}    0.93{col 67}{space 3}0.351{col 75}{space 4}-.3602995{col 88}{space 3} 1.014131
{txt}Perm. Disabled/Homemaker/Student  {c |}{col 35}{res}{space 2} .9003075{col 47}{space 2} .3512696{col 58}{space 1}    2.56{col 67}{space 3}0.010{col 75}{space 4} .2118318{col 88}{space 3} 1.588783
{txt}{space 33} {c |}
{space 26}marital {c |}
{space 25}Married  {c |}{col 35}{res}{space 2}-.5255473{col 47}{space 2} .2411401{col 58}{space 1}   -2.18{col 67}{space 3}0.029{col 75}{space 4}-.9981731{col 88}{space 3}-.0529214
{txt}{space 29}nfc1 {c |}{col 35}{res}{space 2} .1005077{col 47}{space 2} .1641339{col 58}{space 1}    0.61{col 67}{space 3}0.540{col 75}{space 4}-.2211889{col 88}{space 3} .4222043
{txt}{space 24}evaluate1 {c |}{col 35}{res}{space 2}-.3723865{col 47}{space 2} .2776236{col 58}{space 1}   -1.34{col 67}{space 3}0.180{col 75}{space 4}-.9165189{col 88}{space 3} .1717458
{txt}{hline 34}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                            /cut1 {c |}{col 35}{res}{space 2}-3.762997{col 47}{space 2} .7713422{col 75}{space 4}  -5.2748{col 88}{space 3}-2.251194
{txt}                            /cut2 {c |}{col 35}{res}{space 2}-1.388914{col 47}{space 2} .7707576{col 75}{space 4}-2.899571{col 88}{space 3} .1217432
{txt}{hline 34}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-465.09183}  
Iteration 1:{space 3}log pseudolikelihood = {res:-431.54296}  
Iteration 2:{space 3}log pseudolikelihood = {res:-431.12001}  
Iteration 3:{space 3}log pseudolikelihood = {res:-431.11879}  
Iteration 4:{space 3}log pseudolikelihood = {res:-431.11879}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       549
{txt}{col 51}Wald chi2({res}19{txt}){col 67}= {res}     51.86
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0001
{txt}Log pseudolikelihood = {res}-431.11879{txt}{col 51}Pseudo R2{col 67}= {res}    0.0730

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}                       retro2002_3{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      z{col 68}   P>|z|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}partisan2000 {c |}
{space 22}In-Partisan  {c |}{col 36}{res}{space 2}-1.184701{col 48}{space 2} .2632278{col 59}{space 1}   -4.50{col 68}{space 3}0.000{col 76}{space 4}-1.700618{col 89}{space 3}-.6687841
{txt}{space 15}disagree_total_freq {c |}{col 36}{res}{space 2}-.0049168{col 48}{space 2} .0360445{col 59}{space 1}   -0.14{col 68}{space 3}0.891{col 76}{space 4}-.0755627{col 89}{space 3}  .065729
{txt}{space 34} {c |}
partisan2000#c.disagree_total_freq {c |}
{space 22}In-Partisan  {c |}{col 36}{res}{space 2}-.0102942{col 48}{space 2} .0548129{col 59}{space 1}   -0.19{col 68}{space 3}0.851{col 76}{space 4}-.1177255{col 89}{space 3} .0971372
{txt}{space 34} {c |}
{space 28}names1 {c |}{col 36}{res}{space 2}-.2488851{col 48}{space 2} .0903269{col 59}{space 1}   -2.76{col 68}{space 3}0.006{col 76}{space 4}-.4259226{col 89}{space 3}-.0718477
{txt}{space 24}disc_knowl {c |}{col 36}{res}{space 2} .0742019{col 48}{space 2} .2119652{col 59}{space 1}    0.35{col 68}{space 3}0.726{col 76}{space 4}-.3412423{col 89}{space 3}  .489646
{txt}{space 28}follow {c |}{col 36}{res}{space 2} .0343718{col 48}{space 2} .1330456{col 59}{space 1}    0.26{col 68}{space 3}0.796{col 76}{space 4}-.2263928{col 89}{space 3} .2951364
{txt}{space 27}age2002 {c |}{col 36}{res}{space 2}-.0033488{col 48}{space 2} .0090639{col 59}{space 1}   -0.37{col 68}{space 3}0.712{col 76}{space 4}-.0211136{col 89}{space 3} .0144161
{txt}{space 30}educ {c |}{col 36}{res}{space 2}-.0767834{col 48}{space 2} .1318591{col 59}{space 1}   -0.58{col 68}{space 3}0.560{col 76}{space 4}-.3352225{col 89}{space 3} .1816556
{txt}{space 24}income2002 {c |}{col 36}{res}{space 2} .0340496{col 48}{space 2} .0629609{col 59}{space 1}    0.54{col 68}{space 3}0.589{col 76}{space 4}-.0893515{col 89}{space 3} .1574507
{txt}{space 34} {c |}
{space 28}gender {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.2870993{col 48}{space 2} .2147087{col 59}{space 1}   -1.34{col 68}{space 3}0.181{col 76}{space 4}-.7079206{col 89}{space 3} .1337219
{txt}{space 34} {c |}
{space 30}race {c |}
{space 28}Black  {c |}{col 36}{res}{space 2} .1654041{col 48}{space 2}  .423248{col 59}{space 1}    0.39{col 68}{space 3}0.696{col 76}{space 4}-.6641468{col 89}{space 3} .9949549
{txt}{space 25}Hispanic  {c |}{col 36}{res}{space 2} .4588051{col 48}{space 2} .4361686{col 59}{space 1}    1.05{col 68}{space 3}0.293{col 76}{space 4}-.3960697{col 89}{space 3}  1.31368
{txt}{space 28}Other  {c |}{col 36}{res}{space 2} .3016751{col 48}{space 2} .5329063{col 59}{space 1}    0.57{col 68}{space 3}0.571{col 76}{space 4} -.742802{col 89}{space 3} 1.346152
{txt}{space 34} {c |}
{space 26}employed {c |}
{space 23}Unemployed  {c |}{col 36}{res}{space 2} .4509363{col 48}{space 2}  .618256{col 59}{space 1}    0.73{col 68}{space 3}0.466{col 76}{space 4}-.7608231{col 89}{space 3} 1.662696
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2} .3148205{col 48}{space 2} .3494122{col 59}{space 1}    0.90{col 68}{space 3}0.368{col 76}{space 4}-.3700149{col 89}{space 3}  .999656
{txt}{space 1}Perm. Disabled/Homemaker/Student  {c |}{col 36}{res}{space 2} .9024536{col 48}{space 2} .3547013{col 59}{space 1}    2.54{col 68}{space 3}0.011{col 76}{space 4} .2072518{col 89}{space 3} 1.597656
{txt}{space 34} {c |}
{space 27}marital {c |}
{space 26}Married  {c |}{col 36}{res}{space 2}-.5117002{col 48}{space 2} .2413634{col 59}{space 1}   -2.12{col 68}{space 3}0.034{col 76}{space 4}-.9847637{col 89}{space 3}-.0386366
{txt}{space 30}nfc1 {c |}{col 36}{res}{space 2} .1067108{col 48}{space 2} .1650642{col 59}{space 1}    0.65{col 68}{space 3}0.518{col 76}{space 4}-.2168092{col 89}{space 3} .4302307
{txt}{space 25}evaluate1 {c |}{col 36}{res}{space 2}-.3671632{col 48}{space 2} .2792928{col 59}{space 1}   -1.31{col 68}{space 3}0.189{col 76}{space 4}-.9145669{col 89}{space 3} .1802406
{txt}{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                             /cut1 {c |}{col 36}{res}{space 2}-3.740614{col 48}{space 2} .7982331{col 76}{space 4}-5.305122{col 89}{space 3}-2.176106
{txt}                             /cut2 {c |}{col 36}{res}{space 2}-1.369915{col 48}{space 2} .7979437{col 76}{space 4}-2.933856{col 89}{space 3} .1940257
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-465.09183}  
Iteration 1:{space 3}log pseudolikelihood = {res:-431.60805}  
Iteration 2:{space 3}log pseudolikelihood = {res:-431.18459}  
Iteration 3:{space 3}log pseudolikelihood = {res:-431.18338}  
Iteration 4:{space 3}log pseudolikelihood = {res:-431.18338}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       549
{txt}{col 51}Wald chi2({res}19{txt}){col 67}= {res}     52.13
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0001
{txt}Log pseudolikelihood = {res}-431.18338{txt}{col 51}Pseudo R2{col 67}= {res}    0.0729

{txt}{hline 34}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 35}{c |}{col 47}    Robust
{col 1}                      retro2002_3{col 35}{c |}      Coef.{col 47}   Std. Err.{col 59}      z{col 67}   P>|z|{col 75}     [95% Con{col 88}f. Interval]
{hline 34}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}partisan2000 {c |}
{space 21}In-Partisan  {c |}{col 35}{res}{space 2}-1.141637{col 47}{space 2} .2586706{col 58}{space 1}   -4.41{col 67}{space 3}0.000{col 75}{space 4}-1.648622{col 88}{space 3} -.634652
{txt}{space 13}disagree_total_knowl {c |}{col 35}{res}{space 2}-.0072147{col 47}{space 2}   .03291{col 58}{space 1}   -0.22{col 67}{space 3}0.826{col 75}{space 4}-.0717171{col 88}{space 3} .0572877
{txt}{space 33} {c |}
{space 21}partisan2000#{c |}
{space 11}c.disagree_total_knowl {c |}
{space 21}In-Partisan  {c |}{col 35}{res}{space 2} .0053376{col 47}{space 2} .0499808{col 58}{space 1}    0.11{col 67}{space 3}0.915{col 75}{space 4} -.092623{col 88}{space 3} .1032982
{txt}{space 33} {c |}
{space 27}names1 {c |}{col 35}{res}{space 2}-.2451697{col 47}{space 2} .0903712{col 58}{space 1}   -2.71{col 67}{space 3}0.007{col 75}{space 4} -.422294{col 88}{space 3}-.0680455
{txt}{space 23}disc_knowl {c |}{col 35}{res}{space 2}  .074137{col 47}{space 2} .2158133{col 58}{space 1}    0.34{col 67}{space 3}0.731{col 75}{space 4}-.3488493{col 88}{space 3} .4971233
{txt}{space 27}follow {c |}{col 35}{res}{space 2} .0373097{col 47}{space 2} .1327917{col 58}{space 1}    0.28{col 67}{space 3}0.779{col 75}{space 4}-.2229572{col 88}{space 3} .2975766
{txt}{space 26}age2002 {c |}{col 35}{res}{space 2} -.003416{col 47}{space 2} .0090409{col 58}{space 1}   -0.38{col 67}{space 3}0.706{col 75}{space 4}-.0211358{col 88}{space 3} .0143038
{txt}{space 29}educ {c |}{col 35}{res}{space 2}-.0779251{col 47}{space 2} .1321537{col 58}{space 1}   -0.59{col 67}{space 3}0.555{col 75}{space 4}-.3369417{col 88}{space 3} .1810914
{txt}{space 23}income2002 {c |}{col 35}{res}{space 2} .0333178{col 47}{space 2} .0628067{col 58}{space 1}    0.53{col 67}{space 3}0.596{col 75}{space 4} -.089781{col 88}{space 3} .1564166
{txt}{space 33} {c |}
{space 27}gender {c |}
{space 26}Female  {c |}{col 35}{res}{space 2}-.2892483{col 47}{space 2} .2144634{col 58}{space 1}   -1.35{col 67}{space 3}0.177{col 75}{space 4}-.7095888{col 88}{space 3} .1310922
{txt}{space 33} {c |}
{space 29}race {c |}
{space 27}Black  {c |}{col 35}{res}{space 2} .1657185{col 47}{space 2} .4226388{col 58}{space 1}    0.39{col 67}{space 3}0.695{col 75}{space 4}-.6626382{col 88}{space 3} .9940753
{txt}{space 24}Hispanic  {c |}{col 35}{res}{space 2} .4642285{col 47}{space 2} .4369531{col 58}{space 1}    1.06{col 67}{space 3}0.288{col 75}{space 4}-.3921837{col 88}{space 3} 1.320641
{txt}{space 27}Other  {c |}{col 35}{res}{space 2}  .303161{col 47}{space 2} .5330841{col 58}{space 1}    0.57{col 67}{space 3}0.570{col 75}{space 4}-.7416646{col 88}{space 3} 1.347987
{txt}{space 33} {c |}
{space 25}employed {c |}
{space 22}Unemployed  {c |}{col 35}{res}{space 2} .4473209{col 47}{space 2} .6202254{col 58}{space 1}    0.72{col 67}{space 3}0.471{col 75}{space 4}-.7682985{col 88}{space 3}  1.66294
{txt}{space 25}Retired  {c |}{col 35}{res}{space 2}  .314662{col 47}{space 2} .3490376{col 58}{space 1}    0.90{col 67}{space 3}0.367{col 75}{space 4}-.3694391{col 88}{space 3} .9987631
{txt}Perm. Disabled/Homemaker/Student  {c |}{col 35}{res}{space 2} .9147716{col 47}{space 2} .3535721{col 58}{space 1}    2.59{col 67}{space 3}0.010{col 75}{space 4} .2217831{col 88}{space 3}  1.60776
{txt}{space 33} {c |}
{space 26}marital {c |}
{space 25}Married  {c |}{col 35}{res}{space 2}-.5017031{col 47}{space 2} .2396574{col 58}{space 1}   -2.09{col 67}{space 3}0.036{col 75}{space 4} -.971423{col 88}{space 3}-.0319831
{txt}{space 29}nfc1 {c |}{col 35}{res}{space 2} .1066288{col 47}{space 2} .1648141{col 58}{space 1}    0.65{col 67}{space 3}0.518{col 75}{space 4}-.2164008{col 88}{space 3} .4296584
{txt}{space 24}evaluate1 {c |}{col 35}{res}{space 2}-.3627219{col 47}{space 2} .2779322{col 58}{space 1}   -1.31{col 67}{space 3}0.192{col 75}{space 4}-.9074591{col 88}{space 3} .1820153
{txt}{hline 34}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                            /cut1 {c |}{col 35}{res}{space 2}-3.712877{col 47}{space 2} .8100349{col 75}{space 4}-5.300516{col 88}{space 3}-2.125237
{txt}                            /cut2 {c |}{col 35}{res}{space 2}-1.342634{col 47}{space 2} .8098172{col 75}{space 4}-2.929847{col 88}{space 3} .2445784
{txt}{hline 34}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{com}.         
. esttab using 2002_ALTMEASURES_ECON.rtf, onecell nobaselevels replace  label pr2 aic bic se star(+ 0.10 * 0.05 ** 0.01) ///
>         mtitles("Original Measure" "/(D+A)" "Weigh: Disc. Freq" "Weigh: Disc. Knowl")  ///
>         rename(disagree_total Disagreement disagree_avg Disagreement disagree_total_freq Disagreement ///
>                                 disagree_total_knowl Disagreement ///
>                 1.partisan2000#c.disagree_total Partisan*Disagreement 1.partisan2000#c.disagree_avg Partisan*Disagreement  ///
>                 1.partisan2000#c.disagree_total_freq Partisan*Disagreement 1.partisan2000#c.disagree_total_knowl Partisan*Disagreement )
{res}{txt}(output written to {browse  `"2002_ALTMEASURES_ECON.rtf"'})

{com}.                 
. eststo clear
{txt}
{com}. 
. 
. /****************************************
> *****************************************
>                 2006 ANES
> *****************************************
> ****************************************/       
. 
. *data cleaning
. clear
{txt}
{com}. do "Data Cleaning - 2006 ANES.do"
{txt}
{com}. **********************************************************************
. **********************************************************************
. ***********************2006 ANES Pilot Survey*************************
. **********************************************************************
. **********************************************************************
. **********************************************************************
. 
. 
. **********************************************************************
. ****************************Data Cleaning****************************
. **********************************************************************
. **********************************************************************
. **********************************************************************
. **********************************************************************
. clear
{txt}
{com}. set more off
{txt}
{com}. use "ANES2006.dta"
{txt}
{com}. set more off
{txt}
{com}.         
.                 ************************************
.                 *********Economic Assessments*******
.                 ************************************
. 
. recode V06P808 (1=3) (2=2) (3=1) (8=.) (9=.), gen(retro)
{txt}(418 differences between V06P808 and retro)

{com}. label def ret 1 "Worse" 2 "Same" 3 "Better" 
{txt}
{com}. label values retro ret
{txt}
{com}. label var retro "Retrospective Economic Assessments"
{txt}
{com}. tab retro

{txt}Retrospecti {c |}
ve Economic {c |}
Assessments {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
      Worse {c |}{res}        202       30.06       30.06
{txt}       Same {c |}{res}        257       38.24       68.30
{txt}     Better {c |}{res}        213       31.70      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        672      100.00
{txt}
{com}. 
.                 
.                 ************************************
.                 *********Partisanship***************
.                 ************************************
.                 
. ***2006 ANES Pilot
.         *checking the two versions
.         gen version = .
{txt}(1212 missing values generated)

{com}.         replace version = 1 if V06P680a !=.
{txt}(323 real changes made)

{com}.         replace version = 0 if  V06P680b !=.
{txt}(352 real changes made)

{com}.         label def ver 1 "Version 1" 0 "Version 2" 
{txt}
{com}.         label values version ver
{txt}
{com}.         label var version "PID Version"
{txt}
{com}.         
.         tab V06P680

{txt}Mod19_0. R Party ID summary - VERSION 1 {c |}
                          and VERSION 2 {c |}      Freq.     Percent        Cum.
{hline 40}{c +}{hline 35}
             0. Strong Democrat (2/1/.) {c |}{res}        169       25.04       25.04
{txt}           1. Weak Democrat (2/5-8-9/.) {c |}{res}        104       15.41       40.44
{txt}  2. Independent-Democrat (3-4-5-8/./3) {c |}{res}         78       11.56       52.00
{txt}3. Independent-Independent (3-5/./2-8-9 {c |}{res}         40        5.93       57.93
{txt}4. Independent-Republican (3-4-5-8/./1) {c |}{res}         60        8.89       66.81
{txt}         5. Weak Republican (1/5-8-9/.) {c |}{res}         83       12.30       79.11
{txt}           6. Strong Republican (1/1/.) {c |}{res}        132       19.56       98.67
{txt}                   7. Other (4/./2-8-9) {c |}{res}          6        0.89       99.56
{txt}                     9. Refused (9/./.) {c |}{res}          3        0.44      100.00
{txt}{hline 40}{c +}{hline 35}
                                  Total {c |}{res}        675      100.00
{txt}
{com}.         tab V06P680 version, row col chi2
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

  Mod19_0. R Party ID {c |}
  summary - VERSION 1 {c |}      PID Version
        and VERSION 2 {c |} Version 2  Version 1 {c |}     Total
{hline 22}{c +}{hline 22}{c +}{hline 10}
0. Strong Democrat (2 {c |}{res}        77         92 {txt}{c |}{res}       169 
                      {txt}{c |}{res}     45.56      54.44 {txt}{c |}{res}    100.00 
                      {txt}{c |}{res}     21.88      28.48 {txt}{c |}{res}     25.04 
{txt}{hline 22}{c +}{hline 22}{c +}{hline 10}
1. Weak Democrat (2/5 {c |}{res}        55         49 {txt}{c |}{res}       104 
                      {txt}{c |}{res}     52.88      47.12 {txt}{c |}{res}    100.00 
                      {txt}{c |}{res}     15.63      15.17 {txt}{c |}{res}     15.41 
{txt}{hline 22}{c +}{hline 22}{c +}{hline 10}
2. Independent-Democr {c |}{res}        48         30 {txt}{c |}{res}        78 
                      {txt}{c |}{res}     61.54      38.46 {txt}{c |}{res}    100.00 
                      {txt}{c |}{res}     13.64       9.29 {txt}{c |}{res}     11.56 
{txt}{hline 22}{c +}{hline 22}{c +}{hline 10}
3. Independent-Indepe {c |}{res}        23         17 {txt}{c |}{res}        40 
                      {txt}{c |}{res}     57.50      42.50 {txt}{c |}{res}    100.00 
                      {txt}{c |}{res}      6.53       5.26 {txt}{c |}{res}      5.93 
{txt}{hline 22}{c +}{hline 22}{c +}{hline 10}
4. Independent-Republ {c |}{res}        36         24 {txt}{c |}{res}        60 
                      {txt}{c |}{res}     60.00      40.00 {txt}{c |}{res}    100.00 
                      {txt}{c |}{res}     10.23       7.43 {txt}{c |}{res}      8.89 
{txt}{hline 22}{c +}{hline 22}{c +}{hline 10}
5. Weak Republican (1 {c |}{res}        43         40 {txt}{c |}{res}        83 
                      {txt}{c |}{res}     51.81      48.19 {txt}{c |}{res}    100.00 
                      {txt}{c |}{res}     12.22      12.38 {txt}{c |}{res}     12.30 
{txt}{hline 22}{c +}{hline 22}{c +}{hline 10}
6. Strong Republican  {c |}{res}        65         67 {txt}{c |}{res}       132 
                      {txt}{c |}{res}     49.24      50.76 {txt}{c |}{res}    100.00 
                      {txt}{c |}{res}     18.47      20.74 {txt}{c |}{res}     19.56 
{txt}{hline 22}{c +}{hline 22}{c +}{hline 10}
 7. Other (4/./2-8-9) {c |}{res}         2          4 {txt}{c |}{res}         6 
                      {txt}{c |}{res}     33.33      66.67 {txt}{c |}{res}    100.00 
                      {txt}{c |}{res}      0.57       1.24 {txt}{c |}{res}      0.89 
{txt}{hline 22}{c +}{hline 22}{c +}{hline 10}
   9. Refused (9/./.) {c |}{res}         3          0 {txt}{c |}{res}         3 
                      {txt}{c |}{res}    100.00       0.00 {txt}{c |}{res}    100.00 
                      {txt}{c |}{res}      0.85       0.00 {txt}{c |}{res}      0.44 
{txt}{hline 22}{c +}{hline 22}{c +}{hline 10}
                Total {c |}{res}       352        323 {txt}{c |}{res}       675 
                      {txt}{c |}{res}     52.15      47.85 {txt}{c |}{res}    100.00 
                      {txt}{c |}{res}    100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}8{txt}) = {res} 11.7125  {txt} Pr = {res}0.165
{txt}
{com}.         
.         *7-point scale
.         recode V06P680 (0=1) (1=2) (2=3) (3=4) (4=5) (5=6) (6=7) (7=.) (9=.), gen(partyid)
{txt}(675 differences between V06P680 and partyid)

{com}.         label def pi 1 "Strong Dem" 2 "Weak Dem" 3 "Lean Dem" 4 "Ind." 5 "Lean Rep" 6 "Weak Rep" 7 "Strong Rep"
{txt}
{com}.         label values partyid pi
{txt}
{com}.         label var partyid "Party Identification"
{txt}
{com}.         tab partyid

      {txt}Party {c |}
Identificat {c |}
        ion {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
 Strong Dem {c |}{res}        169       25.38       25.38
{txt}   Weak Dem {c |}{res}        104       15.62       40.99
{txt}   Lean Dem {c |}{res}         78       11.71       52.70
{txt}       Ind. {c |}{res}         40        6.01       58.71
{txt}   Lean Rep {c |}{res}         60        9.01       67.72
{txt}   Weak Rep {c |}{res}         83       12.46       80.18
{txt} Strong Rep {c |}{res}        132       19.82      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        666      100.00
{txt}
{com}.         tab partyid version, row col chi2
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

     Party {c |}
Identifica {c |}      PID Version
      tion {c |} Version 2  Version 1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
Strong Dem {c |}{res}        77         92 {txt}{c |}{res}       169 
           {txt}{c |}{res}     45.56      54.44 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     22.19      28.84 {txt}{c |}{res}     25.38 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
  Weak Dem {c |}{res}        55         49 {txt}{c |}{res}       104 
           {txt}{c |}{res}     52.88      47.12 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     15.85      15.36 {txt}{c |}{res}     15.62 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
  Lean Dem {c |}{res}        48         30 {txt}{c |}{res}        78 
           {txt}{c |}{res}     61.54      38.46 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     13.83       9.40 {txt}{c |}{res}     11.71 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
      Ind. {c |}{res}        23         17 {txt}{c |}{res}        40 
           {txt}{c |}{res}     57.50      42.50 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}      6.63       5.33 {txt}{c |}{res}      6.01 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
  Lean Rep {c |}{res}        36         24 {txt}{c |}{res}        60 
           {txt}{c |}{res}     60.00      40.00 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     10.37       7.52 {txt}{c |}{res}      9.01 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
  Weak Rep {c |}{res}        43         40 {txt}{c |}{res}        83 
           {txt}{c |}{res}     51.81      48.19 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     12.39      12.54 {txt}{c |}{res}     12.46 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
Strong Rep {c |}{res}        65         67 {txt}{c |}{res}       132 
           {txt}{c |}{res}     49.24      50.76 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     18.73      21.00 {txt}{c |}{res}     19.82 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       347        319 {txt}{c |}{res}       666 
           {txt}{c |}{res}     52.10      47.90 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}    100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}6{txt}) = {res}  8.1073  {txt} Pr = {res}0.230
{txt}
{com}.         
.         *3-point categorical
.         gen pid_3 = . 
{txt}(1212 missing values generated)

{com}.         replace pid_3 = 1 if partyid >=1 & partyid <= 3
{txt}(351 real changes made)

{com}.         replace pid_3 = 3 if partyid == 4
{txt}(40 real changes made)

{com}.         replace pid_3 = 2 if partyid >=5 & partyid <= 7
{txt}(275 real changes made)

{com}.         label var pid_3 "Party ID (Categorical)"
{txt}
{com}.         label def pi2 1 "Democrat" 3 "Independent" 2 "Republican"
{txt}
{com}.         label values pid_3 pi2
{txt}
{com}.         
.         *Republican vs. Democrat
.         gen pid_2 = . 
{txt}(1212 missing values generated)

{com}.         replace pid_2 = 1 if partyid >=1 & partyid <= 3
{txt}(351 real changes made)

{com}.         replace pid_2 = 0 if partyid >=5 & partyid <= 7
{txt}(275 real changes made)

{com}.         label var pid_2 "PID" 
{txt}
{com}.         label def pi3 1 "Democrat" 0 "Republican"
{txt}
{com}.         label values pid_2 pi3
{txt}
{com}.         
.         tab partyid

      {txt}Party {c |}
Identificat {c |}
        ion {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
 Strong Dem {c |}{res}        169       25.38       25.38
{txt}   Weak Dem {c |}{res}        104       15.62       40.99
{txt}   Lean Dem {c |}{res}         78       11.71       52.70
{txt}       Ind. {c |}{res}         40        6.01       58.71
{txt}   Lean Rep {c |}{res}         60        9.01       67.72
{txt}   Weak Rep {c |}{res}         83       12.46       80.18
{txt} Strong Rep {c |}{res}        132       19.82      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        666      100.00
{txt}
{com}.         tab pid_3

   {txt}Party ID {c |}
(Categorica {c |}
         l) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
   Democrat {c |}{res}        351       52.70       52.70
{txt} Republican {c |}{res}        275       41.29       93.99
{txt}Independent {c |}{res}         40        6.01      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        666      100.00
{txt}
{com}.         tab pid_2

        {txt}PID {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
 Republican {c |}{res}        275       43.93       43.93
{txt}   Democrat {c |}{res}        351       56.07      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        626      100.00
{txt}
{com}.         tab pid_2 version, row col chi2
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

           {c |}      PID Version
       PID {c |} Version 2  Version 1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
Republican {c |}{res}       144        131 {txt}{c |}{res}       275 
           {txt}{c |}{res}     52.36      47.64 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     44.44      43.38 {txt}{c |}{res}     43.93 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
  Democrat {c |}{res}       180        171 {txt}{c |}{res}       351 
           {txt}{c |}{res}     51.28      48.72 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     55.56      56.62 {txt}{c |}{res}     56.07 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       324        302 {txt}{c |}{res}       626 
           {txt}{c |}{res}     51.76      48.24 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}    100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}1{txt}) = {res}  0.0722  {txt} Pr = {res}0.788
{txt}
{com}.         
.         *Partisan
.         recode pid_2 (1=0) (0=1), gen(partisan)
{txt}(626 differences between pid_2 and partisan)

{com}.         label var partisan "Co-Partisan to Inc. President"
{txt}
{com}.         label def part1 1 "In-Partisan" 0 "Out-Partisan"
{txt}
{com}.         label values partisan part1
{txt}
{com}. 
. *PID Strength (Non-Independents)*
.         gen pid_str = . 
{txt}(1212 missing values generated)

{com}.         replace pid_str = 1 if partyid == 3
{txt}(78 real changes made)

{com}.         replace pid_str = 1 if partyid == 5
{txt}(60 real changes made)

{com}.         replace pid_str = 2 if partyid == 2
{txt}(104 real changes made)

{com}.         replace pid_str = 2 if partyid == 6
{txt}(83 real changes made)

{com}.         replace pid_str = 3 if partyid == 1
{txt}(169 real changes made)

{com}.         replace pid_str = 3 if partyid == 7
{txt}(132 real changes made)

{com}.         label var pid_str "PID Str."
{txt}
{com}.         label def pi4 1 "Leaner" 2 "Weak" 3 "Strong"
{txt}
{com}.         label values pid_str pi4
{txt}
{com}. 
.         tab pid_str     

   {txt}PID Str. {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
     Leaner {c |}{res}        138       22.04       22.04
{txt}       Weak {c |}{res}        187       29.87       51.92
{txt}     Strong {c |}{res}        301       48.08      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        626      100.00
{txt}
{com}.         tab pid_str version, row col chi2
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

           {c |}      PID Version
  PID Str. {c |} Version 2  Version 1 {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
    Leaner {c |}{res}        84         54 {txt}{c |}{res}       138 
           {txt}{c |}{res}     60.87      39.13 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     25.93      17.88 {txt}{c |}{res}     22.04 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
      Weak {c |}{res}        98         89 {txt}{c |}{res}       187 
           {txt}{c |}{res}     52.41      47.59 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     30.25      29.47 {txt}{c |}{res}     29.87 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
    Strong {c |}{res}       142        159 {txt}{c |}{res}       301 
           {txt}{c |}{res}     47.18      52.82 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     43.83      52.65 {txt}{c |}{res}     48.08 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       324        302 {txt}{c |}{res}       626 
           {txt}{c |}{res}     51.76      48.24 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}    100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}2{txt}) = {res}  7.1507  {txt} Pr = {res}0.028
{txt}
{com}.         
.         summ pid_str

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 5}pid_str {c |}{res}       626    2.260383    .7965501          1          3
{txt}
{com}.         gen pid_str01 = (pid_str - r(min))/(r(max)-r(min))
{txt}(586 missing values generated)

{com}.         label var pid_str01 "PID Str."
{txt}
{com}.         
.         
. *PID Strength (Full)
.         recode partyid (1=4) (2=3) (3=2) (4=1) (5=2) (6=3) (7=4), gen(pid_str_full)
{txt}(666 differences between partyid and pid_str_full)

{com}.         label var pid_str_full "PID Str."
{txt}
{com}.         label def pi5 1 "Ind" 2 "Leaner" 3 "Weak" 4 "Strong"
{txt}
{com}.         label values pid_str_full pi5
{txt}
{com}. 
. 
. ***2004 ANES TS
.         *7-pt scale*
.         recode  V043116 (0=1) (1=2) (2=3) (3=4) (4=5) (5=6) (6=7) (7=.) (8=.) (9=.), gen(partyid04)
{txt}(1212 differences between V043116 and partyid04)

{com}.         label var partyid04 "PID - 2004"
{txt}
{com}.         label values partyid04 pi 
{txt}
{com}.         *3-pt
.         gen pid_304 = . 
{txt}(1212 missing values generated)

{com}.         replace pid_304 = 1 if partyid04 >=1 & partyid04 <= 3
{txt}(592 real changes made)

{com}.         replace pid_304 = 3 if partyid04 == 4
{txt}(118 real changes made)

{com}.         replace pid_304 = 2 if partyid04 >=5 & partyid04 <= 7
{txt}(485 real changes made)

{com}.         label var pid_304 "Party ID (Categorical; 2004)"
{txt}
{com}.         label values pid_304 pi2
{txt}
{com}.         *Republican vs. Democrat*
.         gen pid_204 = . 
{txt}(1212 missing values generated)

{com}.         replace pid_204 = 1 if partyid04 >=1 & partyid04 <= 3
{txt}(592 real changes made)

{com}.         replace pid_204 = 0 if partyid04 >=5 & partyid04 <= 7
{txt}(485 real changes made)

{com}.         label var pid_204 "PID" 
{txt}
{com}.         label values pid_204 pi3
{txt}
{com}.         
.         *Partisan
.                 recode pid_204 (1=0) (0=1), gen(partisan2004)
{txt}(1077 differences between pid_204 and partisan2004)

{com}.                 label values partisan2004 part1
{txt}
{com}.                 
.         
.         *PID Strength
.                 recode partyid04 (1=4) (2=3) (3=2) (4=1) (5=2) (6=3) (7=4), gen(pid_str2004_full)
{txt}(1195 differences between partyid04 and pid_str2004_full)

{com}.                 label values pid_str2004_full pi5               
{txt}
{com}.                 
.                 recode partyid04 (1=3) (2=2) (3=1) (4=.) (5=1) (6=2) (7=3), gen(pid_str2004)
{txt}(1016 differences between partyid04 and pid_str2004)

{com}.                 label values pid_str2004 pi4    
{txt}
{com}.                 
.                 label var pid_str2004_full "PID Str."
{txt}
{com}.                 label var pid_str2004 "PID Str."
{txt}
{com}.                 
.         
.         *Relationship with 2006
.         tab partyid partyid04, row col chi2
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

     Party {c |}
Identifica {c |}                                  PID - 2004
      tion {c |} Strong De   Weak Dem   Lean Dem       Ind.   Lean Rep   Weak Rep  Strong Re {c |}     Total
{hline 11}{c +}{hline 77}{c +}{hline 10}
Strong Dem {c |}{res}        98         32         29          3          2          3          2 {txt}{c |}{res}       169 
           {txt}{c |}{res}     57.99      18.93      17.16       1.78       1.18       1.78       1.18 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     86.73      36.78      26.36       6.12       2.67       2.94       1.63 {txt}{c |}{res}     25.64 
{txt}{hline 11}{c +}{hline 77}{c +}{hline 10}
  Weak Dem {c |}{res}         6         44         25          8          6         12          1 {txt}{c |}{res}       102 
           {txt}{c |}{res}      5.88      43.14      24.51       7.84       5.88      11.76       0.98 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}      5.31      50.57      22.73      16.33       8.00      11.76       0.81 {txt}{c |}{res}     15.48 
{txt}{hline 11}{c +}{hline 77}{c +}{hline 10}
  Lean Dem {c |}{res}         6          5         42          8         10          4          2 {txt}{c |}{res}        77 
           {txt}{c |}{res}      7.79       6.49      54.55      10.39      12.99       5.19       2.60 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}      5.31       5.75      38.18      16.33      13.33       3.92       1.63 {txt}{c |}{res}     11.68 
{txt}{hline 11}{c +}{hline 77}{c +}{hline 10}
      Ind. {c |}{res}         1          3          6         20          6          2          0 {txt}{c |}{res}        38 
           {txt}{c |}{res}      2.63       7.89      15.79      52.63      15.79       5.26       0.00 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}      0.88       3.45       5.45      40.82       8.00       1.96       0.00 {txt}{c |}{res}      5.77 
{txt}{hline 11}{c +}{hline 77}{c +}{hline 10}
  Lean Rep {c |}{res}         0          1          7          7         23         13          8 {txt}{c |}{res}        59 
           {txt}{c |}{res}      0.00       1.69      11.86      11.86      38.98      22.03      13.56 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}      0.00       1.15       6.36      14.29      30.67      12.75       6.50 {txt}{c |}{res}      8.95 
{txt}{hline 11}{c +}{hline 77}{c +}{hline 10}
  Weak Rep {c |}{res}         1          2          1          2         16         48         13 {txt}{c |}{res}        83 
           {txt}{c |}{res}      1.20       2.41       1.20       2.41      19.28      57.83      15.66 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}      0.88       2.30       0.91       4.08      21.33      47.06      10.57 {txt}{c |}{res}     12.59 
{txt}{hline 11}{c +}{hline 77}{c +}{hline 10}
Strong Rep {c |}{res}         1          0          0          1         12         20         97 {txt}{c |}{res}       131 
           {txt}{c |}{res}      0.76       0.00       0.00       0.76       9.16      15.27      74.05 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}      0.88       0.00       0.00       2.04      16.00      19.61      78.86 {txt}{c |}{res}     19.88 
{txt}{hline 11}{c +}{hline 77}{c +}{hline 10}
     Total {c |}{res}       113         87        110         49         75        102        123 {txt}{c |}{res}       659 
           {txt}{c |}{res}     17.15      13.20      16.69       7.44      11.38      15.48      18.66 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}    100.00     100.00     100.00     100.00     100.00     100.00     100.00 {txt}{c |}{res}    100.00 

{txt}         Pearson chi2({res}36{txt}) = {res} 1.0e+03  {txt} Pr = {res}0.000
{txt}
{com}.         tab pid_3 pid_304, row col chi2
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

   Party ID {c |}
(Categorica {c |}   Party ID (Categorical; 2004)
         l) {c |}  Democrat  Republica  Independe {c |}     Total
{hline 12}{c +}{hline 33}{c +}{hline 10}
   Democrat {c |}{res}       287         42         19 {txt}{c |}{res}       348 
            {txt}{c |}{res}     82.47      12.07       5.46 {txt}{c |}{res}    100.00 
            {txt}{c |}{res}     92.58      14.00      38.78 {txt}{c |}{res}     52.81 
{txt}{hline 12}{c +}{hline 33}{c +}{hline 10}
 Republican {c |}{res}        13        250         10 {txt}{c |}{res}       273 
            {txt}{c |}{res}      4.76      91.58       3.66 {txt}{c |}{res}    100.00 
            {txt}{c |}{res}      4.19      83.33      20.41 {txt}{c |}{res}     41.43 
{txt}{hline 12}{c +}{hline 33}{c +}{hline 10}
Independent {c |}{res}        10          8         20 {txt}{c |}{res}        38 
            {txt}{c |}{res}     26.32      21.05      52.63 {txt}{c |}{res}    100.00 
            {txt}{c |}{res}      3.23       2.67      40.82 {txt}{c |}{res}      5.77 
{txt}{hline 12}{c +}{hline 33}{c +}{hline 10}
      Total {c |}{res}       310        300         49 {txt}{c |}{res}       659 
            {txt}{c |}{res}     47.04      45.52       7.44 {txt}{c |}{res}    100.00 
            {txt}{c |}{res}    100.00     100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}4{txt}) = {res}529.2530  {txt} Pr = {res}0.000
{txt}
{com}.         tab pid_2 pid_204, row col chi2
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

           {c |}          PID
       PID {c |} Republica   Democrat {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
Republican {c |}{res}       250         13 {txt}{c |}{res}       263 
           {txt}{c |}{res}     95.06       4.94 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     85.62       4.33 {txt}{c |}{res}     44.43 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
  Democrat {c |}{res}        42        287 {txt}{c |}{res}       329 
           {txt}{c |}{res}     12.77      87.23 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     14.38      95.67 {txt}{c |}{res}     55.57 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       292        300 {txt}{c |}{res}       592 
           {txt}{c |}{res}     49.32      50.68 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}    100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}1{txt}) = {res}395.9814  {txt} Pr = {res}0.000
{txt}
{com}.         pwcorr partyid partyid04, sig

             {txt}{c |}  partyid party~04
{hline 13}{c +}{hline 18}
     partyid {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
   partyid04 {c |} {res}  0.8321   1.0000 
             {txt}{c |}{res}   0.0000
             {txt}{c |}

{com}.         
. ***Bivariate Relationship with Economic Assessments**
.         tab retro pid_2, row col chi2
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

Retrospect {c |}
       ive {c |}
  Economic {c |}
Assessment {c |}          PID
         s {c |} Republica   Democrat {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
     Worse {c |}{res}        35        146 {txt}{c |}{res}       181 
           {txt}{c |}{res}     19.34      80.66 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     12.77      41.71 {txt}{c |}{res}     29.01 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
      Same {c |}{res}       101        138 {txt}{c |}{res}       239 
           {txt}{c |}{res}     42.26      57.74 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     36.86      39.43 {txt}{c |}{res}     38.30 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
    Better {c |}{res}       138         66 {txt}{c |}{res}       204 
           {txt}{c |}{res}     67.65      32.35 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     50.36      18.86 {txt}{c |}{res}     32.69 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       274        350 {txt}{c |}{res}       624 
           {txt}{c |}{res}     43.91      56.09 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}    100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}2{txt}) = {res} 91.3097  {txt} Pr = {res}0.000
{txt}
{com}.         tab retro pid_204, row col chi2
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

Retrospect {c |}
       ive {c |}
  Economic {c |}
Assessment {c |}          PID
         s {c |} Republica   Democrat {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
     Worse {c |}{res}        44        131 {txt}{c |}{res}       175 
           {txt}{c |}{res}     25.14      74.86 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     14.57      41.85 {txt}{c |}{res}     28.46 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
      Same {c |}{res}       112        126 {txt}{c |}{res}       238 
           {txt}{c |}{res}     47.06      52.94 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     37.09      40.26 {txt}{c |}{res}     38.70 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
    Better {c |}{res}       146         56 {txt}{c |}{res}       202 
           {txt}{c |}{res}     72.28      27.72 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     48.34      17.89 {txt}{c |}{res}     32.85 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       302        313 {txt}{c |}{res}       615 
           {txt}{c |}{res}     49.11      50.89 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}    100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}2{txt}) = {res} 84.0041  {txt} Pr = {res}0.000
{txt}
{com}. 
.                 
.                 
. 
.                 
.                 *****************************************************
.                 *********Network Size and Disagreement***************
.                 *****************************************************
.                 
. *Name Generator Version*
.         recode  V06P406 (1=0) (2=1), gen(net_version)
{txt}(675 differences between V06P406 and net_version)

{com}.         label var net_version "Name Generator Version"
{txt}
{com}.         label def net1 1 "Gov't/Elections Generator" 0 "Important Things Generator"
{txt}
{com}.         label values net_version net1
{txt}
{com}.         tab net_version

    {txt}Name Generator Version {c |}      Freq.     Percent        Cum.
{hline 27}{c +}{hline 35}
Important Things Generator {c |}{res}        354       52.44       52.44
{txt} Gov't/Elections Generator {c |}{res}        321       47.56      100.00
{txt}{hline 27}{c +}{hline 35}
                     Total {c |}{res}        675      100.00
{txt}
{com}. 
. *Number of Names Given*
.         gen numgiven = V06P588
{txt}(537 missing values generated)

{com}.         replace numgiven = . if numgiven == 99
{txt}(13 real changes made, 13 to missing)

{com}.         label var numgiven "Number of Listed Discussants"
{txt}
{com}.         tab numgiven

  {txt}Number of {c |}
     Listed {c |}
Discussants {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        186       28.10       28.10
{txt}          1 {c |}{res}         49        7.40       35.50
{txt}          2 {c |}{res}         84       12.69       48.19
{txt}          3 {c |}{res}        129       19.49       67.67
{txt}          4 {c |}{res}         71       10.73       78.40
{txt}          5 {c |}{res}         47        7.10       85.50
{txt}          6 {c |}{res}         19        2.87       88.37
{txt}          7 {c |}{res}         23        3.47       91.84
{txt}          8 {c |}{res}         11        1.66       93.50
{txt}          9 {c |}{res}          7        1.06       94.56
{txt}         10 {c |}{res}         36        5.44      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        662      100.00
{txt}
{com}.         summ numgiven, detail

                {txt}Number of Listed Discussants
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}        662
{txt}25%    {res}        0              0       {txt}Sum of Wgt. {res}        662

{txt}50%    {res}        3                      {txt}Mean          {res} 2.883686
                        {txt}Largest       Std. Dev.     {res} 2.759674
{txt}75%    {res}        4             10
{txt}90%    {res}        7             10       {txt}Variance      {res}   7.6158
{txt}95%    {res}       10             10       {txt}Skewness      {res} 1.003228
{txt}99%    {res}       10             10       {txt}Kurtosis      {res} 3.464902
{txt}
{com}.         
.         gen numgiven1 = numgiven
{txt}(550 missing values generated)

{com}.         replace numgiven1 = 3 if numgiven >= 3 & numgiven <=10
{txt}(214 real changes made)

{com}.         label var numgiven1 "Number of Disc. Asked About"
{txt}
{com}.         tab numgiven1

  {txt}Number of {c |}
Disc. Asked {c |}
      About {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        186       28.10       28.10
{txt}          1 {c |}{res}         49        7.40       35.50
{txt}          2 {c |}{res}         84       12.69       48.19
{txt}          3 {c |}{res}        343       51.81      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        662      100.00
{txt}
{com}.         tab numgiven numgiven1

 {txt}Number of {c |}
    Listed {c |}
Discussant {c |}         Number of Disc. Asked About
         s {c |}         0          1          2          3 {c |}     Total
{hline 11}{c +}{hline 44}{c +}{hline 10}
         0 {c |}{res}       186          0          0          0 {txt}{c |}{res}       186 
{txt}         1 {c |}{res}         0         49          0          0 {txt}{c |}{res}        49 
{txt}         2 {c |}{res}         0          0         84          0 {txt}{c |}{res}        84 
{txt}         3 {c |}{res}         0          0          0        129 {txt}{c |}{res}       129 
{txt}         4 {c |}{res}         0          0          0         71 {txt}{c |}{res}        71 
{txt}         5 {c |}{res}         0          0          0         47 {txt}{c |}{res}        47 
{txt}         6 {c |}{res}         0          0          0         19 {txt}{c |}{res}        19 
{txt}         7 {c |}{res}         0          0          0         23 {txt}{c |}{res}        23 
{txt}         8 {c |}{res}         0          0          0         11 {txt}{c |}{res}        11 
{txt}         9 {c |}{res}         0          0          0          7 {txt}{c |}{res}         7 
{txt}        10 {c |}{res}         0          0          0         36 {txt}{c |}{res}        36 
{txt}{hline 11}{c +}{hline 44}{c +}{hline 10}
     Total {c |}{res}       186         49         84        343 {txt}{c |}{res}       662 

{txt}
{com}.         
.         gen names = numgiven1 
{txt}(550 missing values generated)

{com}.         replace names = . if names == 0
{txt}(186 real changes made, 186 to missing)

{com}.         
.         summ names

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 7}names {c |}{res}       476    2.617647    .6655615          1          3
{txt}
{com}.         gen numgiven01 = (names - r(min))/(r(max)-r(min))
{txt}(736 missing values generated)

{com}.         label var numgiven01 "Network Size"
{txt}
{com}.         
.         
.         
. *****Partisan Disagreement
.         
. *Discussant Partisanship*
.         foreach var in V06P603x V06P608x V06P613x {c -(}
{txt}  2{com}.                 tab `var'
{txt}  3{com}.                 {c )-}

  {txt}Mod13_8_1x. SUMMARY: Party ID network {c |}
                             mention #1 {c |}      Freq.     Percent        Cum.
{hline 40}{c +}{hline 35}
             0. Strong Democrat (1/1/.) {c |}{res}        139       29.20       29.20
{txt}           1. Weak Democrat (1/2-8-9/.) {c |}{res}         58       12.18       41.39
{txt}  2. Independent-Democrat (3-4-5-8/./1) {c |}{res}         49       10.29       51.68
{txt}3. Independent-Independent (3-5/./5-8-9 {c |}{res}         14        2.94       54.62
{txt}4. Independent-Republican (3-4-5-8/./3) {c |}{res}         34        7.14       61.76
{txt}         5. Weak Republican (2/2-8-9/.) {c |}{res}         52       10.92       72.69
{txt}           6. Strong Republican (2/1/.) {c |}{res}        124       26.05       98.74
{txt}                   7. Other (4/./2-8-9) {c |}{res}          2        0.42       99.16
{txt}                8. Don't know (8/./8-9) {c |}{res}          4        0.84      100.00
{txt}{hline 40}{c +}{hline 35}
                                  Total {c |}{res}        476      100.00

  {txt}Mod13_8_2x. SUMMARY: Party ID network {c |}
                             mention #2 {c |}      Freq.     Percent        Cum.
{hline 40}{c +}{hline 35}
             0. Strong Democrat (1/1/.) {c |}{res}        126       29.51       29.51
{txt}           1. Weak Democrat (1/2-8-9/.) {c |}{res}         65       15.22       44.73
{txt}  2. Independent-Democrat (3-4-5-8/./1) {c |}{res}         32        7.49       52.22
{txt}3. Independent-Independent (3-5/./5-8-9 {c |}{res}         11        2.58       54.80
{txt}4. Independent-Republican (3-4-5-8/./3) {c |}{res}         28        6.56       61.36
{txt}         5. Weak Republican (2/2-8-9/.) {c |}{res}         57       13.35       74.71
{txt}           6. Strong Republican (2/1/.) {c |}{res}        102       23.89       98.59
{txt}                   7. Other (4/./2-8-9) {c |}{res}          1        0.23       98.83
{txt}                8. Don't know (8/./8-9) {c |}{res}          5        1.17      100.00
{txt}{hline 40}{c +}{hline 35}
                                  Total {c |}{res}        427      100.00

  {txt}Mod13_8_3x. SUMMARY: Party ID network {c |}
                             mention #3 {c |}      Freq.     Percent        Cum.
{hline 40}{c +}{hline 35}
             0. Strong Democrat (1/1/.) {c |}{res}         94       27.41       27.41
{txt}           1. Weak Democrat (1/2-8-9/.) {c |}{res}         55       16.03       43.44
{txt}  2. Independent-Democrat (3-4-5-8/./1) {c |}{res}         38       11.08       54.52
{txt}3. Independent-Independent (3-5/./5-8-9 {c |}{res}          7        2.04       56.56
{txt}4. Independent-Republican (3-4-5-8/./3) {c |}{res}         19        5.54       62.10
{txt}         5. Weak Republican (2/2-8-9/.) {c |}{res}         41       11.95       74.05
{txt}           6. Strong Republican (2/1/.) {c |}{res}         84       24.49       98.54
{txt}                8. Don't know (8/./8-9) {c |}{res}          5        1.46      100.00
{txt}{hline 40}{c +}{hline 35}
                                  Total {c |}{res}        343      100.00
{txt}
{com}.                 *0-2: Democrat
.                 *3: Independent
.                 *4-6: Republican
.                 
. *# and Avg. Disagreeable Partners
.         *Number Agreeable
.                 *Agreeable = Dem/Dem, Ind/Ind, Rep/Rep
.                 label def agr 1 "PID Agree" 0 "PID Disagree" 
{txt}
{com}.                 gen d1_agree = . 
{txt}(1212 missing values generated)

{com}.                 replace d1_agree = 1 if pid_3 == 1 & V06P603x >=0 & V06P603x <=2
{txt}(190 real changes made)

{com}.                 replace d1_agree = 1 if pid_3 == 2 & V06P603x >=4 & V06P603x <=6 
{txt}(155 real changes made)

{com}.                 replace d1_agree = 1 if pid_3 == 3 & V06P603x == 3
{txt}(8 real changes made)

{com}.                 
.                 replace d1_agree = 0 if pid_3 == 1 & V06P603x >=4 & V06P603x <=6
{txt}(49 real changes made)

{com}.                 replace d1_agree = 0 if pid_3 == 1 & V06P603x == 3
{txt}(4 real changes made)

{com}.                 replace d1_agree = 0 if pid_3 == 2 & V06P603x >=0 & V06P603x <=2
{txt}(48 real changes made)

{com}.                 replace d1_agree = 0 if pid_3 == 2 & V06P603x == 3
{txt}(1 real change made)

{com}.                 replace d1_agree = 0 if pid_3 == 3 & V06P603x >=0 & V06P603x <=2
{txt}(5 real changes made)

{com}.                 replace d1_agree = 0 if pid_3 == 3 & V06P603x >=4 & V06P603x <=6
{txt}(6 real changes made)

{com}.                 label var d1_agree "Agree with D1?"
{txt}
{com}.                 label values d1_agree agr
{txt}
{com}.                 
.                 gen d2_agree = . 
{txt}(1212 missing values generated)

{com}.                 replace d2_agree = 1 if pid_3 == 1 & V06P608x >=0 & V06P608x <=2
{txt}(162 real changes made)

{com}.                 replace d2_agree = 1 if pid_3 == 2 & V06P608x >=4 & V06P608x <=6 
{txt}(128 real changes made)

{com}.                 replace d2_agree = 1 if pid_3 == 3 & V06P608x == 3
{txt}(5 real changes made)

{com}.                 replace d2_agree = 0 if pid_3 == 1 & V06P608x >=4 & V06P608x <=6
{txt}(54 real changes made)

{com}.                 replace d2_agree = 0 if pid_3 == 1 & V06P608x == 3
{txt}(3 real changes made)

{com}.                 replace d2_agree = 0 if pid_3 == 2 & V06P608x >=0 & V06P608x <=2
{txt}(55 real changes made)

{com}.                 replace d2_agree = 0 if pid_3 == 2 & V06P608x == 3
{txt}(3 real changes made)

{com}.                 replace d2_agree = 0 if pid_3 == 3 & V06P608x >=0 & V06P608x <=2
{txt}(5 real changes made)

{com}.                 replace d2_agree = 0 if pid_3 == 3 & V06P608x >=4 & V06P608x <=6
{txt}(5 real changes made)

{com}.                 label var d2_agree "Agree with D2?"
{txt}
{com}.                 label values d2_agree agr
{txt}
{com}.                 
.                 gen d3_agree = . 
{txt}(1212 missing values generated)

{com}.                 replace d3_agree = 1 if pid_3 == 1 & V06P613x >=0 & V06P613x <=2
{txt}(134 real changes made)

{com}.                 replace d3_agree = 1 if pid_3 == 2 & V06P613x >=4 & V06P613x <=6 
{txt}(102 real changes made)

{com}.                 replace d3_agree = 1 if pid_3 == 3 & V06P613x == 3
{txt}(1 real change made)

{com}.                 replace d3_agree = 0 if pid_3 == 1 & V06P613x >=4 & V06P613x <=6
{txt}(39 real changes made)

{com}.                 replace d3_agree = 0 if pid_3 == 1 & V06P613x == 3
{txt}(2 real changes made)

{com}.                 replace d3_agree = 0 if pid_3 == 2 & V06P613x >=0 & V06P613x <=2
{txt}(45 real changes made)

{com}.                 replace d3_agree = 0 if pid_3 == 2 & V06P613x == 3
{txt}(4 real changes made)

{com}.                 replace d3_agree = 0 if pid_3 == 3 & V06P613x >=0 & V06P613x <=2
{txt}(7 real changes made)

{com}.                 replace d3_agree = 0 if pid_3 == 3 & V06P613x >=4 & V06P613x <=6
{txt}(3 real changes made)

{com}.                 label var d3_agree "Agree with D3?"
{txt}
{com}.                 label values d3_agree agr
{txt}
{com}.                 
.                 tab d1_agree

  {txt}Agree with {c |}
         D1? {c |}      Freq.     Percent        Cum.
{hline 13}{c +}{hline 35}
PID Disagree {c |}{res}        113       24.25       24.25
{txt}   PID Agree {c |}{res}        353       75.75      100.00
{txt}{hline 13}{c +}{hline 35}
       Total {c |}{res}        466      100.00
{txt}
{com}.                 tab d2_agree

  {txt}Agree with {c |}
         D2? {c |}      Freq.     Percent        Cum.
{hline 13}{c +}{hline 35}
PID Disagree {c |}{res}        125       29.76       29.76
{txt}   PID Agree {c |}{res}        295       70.24      100.00
{txt}{hline 13}{c +}{hline 35}
       Total {c |}{res}        420      100.00
{txt}
{com}.                 tab d3_agree

  {txt}Agree with {c |}
         D3? {c |}      Freq.     Percent        Cum.
{hline 13}{c +}{hline 35}
PID Disagree {c |}{res}        100       29.67       29.67
{txt}   PID Agree {c |}{res}        237       70.33      100.00
{txt}{hline 13}{c +}{hline 35}
       Total {c |}{res}        337      100.00
{txt}
{com}.                 tab d1_agree d2_agree, row col chi2
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

  Agree with {c |}    Agree with D2?
         D1? {c |} PID Disag  PID Agree {c |}     Total
{hline 13}{c +}{hline 22}{c +}{hline 10}
PID Disagree {c |}{res}        44         57 {txt}{c |}{res}       101 
             {txt}{c |}{res}     43.56      56.44 {txt}{c |}{res}    100.00 
             {txt}{c |}{res}     35.77      19.39 {txt}{c |}{res}     24.22 
{txt}{hline 13}{c +}{hline 22}{c +}{hline 10}
   PID Agree {c |}{res}        79        237 {txt}{c |}{res}       316 
             {txt}{c |}{res}     25.00      75.00 {txt}{c |}{res}    100.00 
             {txt}{c |}{res}     64.23      80.61 {txt}{c |}{res}     75.78 
{txt}{hline 13}{c +}{hline 22}{c +}{hline 10}
       Total {c |}{res}       123        294 {txt}{c |}{res}       417 
             {txt}{c |}{res}     29.50      70.50 {txt}{c |}{res}    100.00 
             {txt}{c |}{res}    100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}1{txt}) = {res} 12.6839  {txt} Pr = {res}0.000
{txt}
{com}.                 tab d1_agree d3_agree, row col chi2
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

  Agree with {c |}    Agree with D3?
         D1? {c |} PID Disag  PID Agree {c |}     Total
{hline 13}{c +}{hline 22}{c +}{hline 10}
PID Disagree {c |}{res}        32         45 {txt}{c |}{res}        77 
             {txt}{c |}{res}     41.56      58.44 {txt}{c |}{res}    100.00 
             {txt}{c |}{res}     32.65      19.15 {txt}{c |}{res}     23.12 
{txt}{hline 13}{c +}{hline 22}{c +}{hline 10}
   PID Agree {c |}{res}        66        190 {txt}{c |}{res}       256 
             {txt}{c |}{res}     25.78      74.22 {txt}{c |}{res}    100.00 
             {txt}{c |}{res}     67.35      80.85 {txt}{c |}{res}     76.88 
{txt}{hline 13}{c +}{hline 22}{c +}{hline 10}
       Total {c |}{res}        98        235 {txt}{c |}{res}       333 
             {txt}{c |}{res}     29.43      70.57 {txt}{c |}{res}    100.00 
             {txt}{c |}{res}    100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}1{txt}) = {res}  7.0948  {txt} Pr = {res}0.008
{txt}
{com}.                 tab d2_agree d3_agree, row col chi
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

  Agree with {c |}    Agree with D3?
         D2? {c |} PID Disag  PID Agree {c |}     Total
{hline 13}{c +}{hline 22}{c +}{hline 10}
PID Disagree {c |}{res}        37         64 {txt}{c |}{res}       101 
             {txt}{c |}{res}     36.63      63.37 {txt}{c |}{res}    100.00 
             {txt}{c |}{res}     37.37      27.12 {txt}{c |}{res}     30.15 
{txt}{hline 13}{c +}{hline 22}{c +}{hline 10}
   PID Agree {c |}{res}        62        172 {txt}{c |}{res}       234 
             {txt}{c |}{res}     26.50      73.50 {txt}{c |}{res}    100.00 
             {txt}{c |}{res}     62.63      72.88 {txt}{c |}{res}     69.85 
{txt}{hline 13}{c +}{hline 22}{c +}{hline 10}
       Total {c |}{res}        99        236 {txt}{c |}{res}       335 
             {txt}{c |}{res}     29.55      70.45 {txt}{c |}{res}    100.00 
             {txt}{c |}{res}    100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}1{txt}) = {res}  3.4828  {txt} Pr = {res}0.062
{txt}
{com}.                 
.                 
.                 *Number Disagreeble
.                 label def dagr 1 "PID Disgree" 0 "PID Agree" 
{txt}
{com}.                 gen d1_disagree = . 
{txt}(1212 missing values generated)

{com}.                 replace d1_disagree = 0 if pid_3 == 1 & V06P603x >=0 & V06P603x <=2
{txt}(190 real changes made)

{com}.                 replace d1_disagree = 0 if pid_3 == 2 & V06P603x >=4 & V06P603x <=6 
{txt}(155 real changes made)

{com}.                 replace d1_disagree = 0 if pid_3 == 3 & V06P603x == 3
{txt}(8 real changes made)

{com}.                 replace d1_disagree = 1 if pid_3 == 1 & V06P603x >=4 & V06P603x <=6
{txt}(49 real changes made)

{com}.                 replace d1_disagree = 1 if pid_3 == 1 & V06P603x == 3
{txt}(4 real changes made)

{com}.                 replace d1_disagree = 1 if pid_3 == 2 & V06P603x >=0 & V06P603x <=2
{txt}(48 real changes made)

{com}.                 replace d1_disagree = 1 if pid_3 == 2 & V06P603x == 3
{txt}(1 real change made)

{com}.                 replace d1_disagree = 1 if pid_3 == 3 & V06P603x >=0 & V06P603x <=2
{txt}(5 real changes made)

{com}.                 replace d1_disagree = 1 if pid_3 == 3 & V06P603x >=4 & V06P603x <=6
{txt}(6 real changes made)

{com}.                 label var d1_disagree "Disagree with D1?"
{txt}
{com}.                 label values d1_disagree dagr
{txt}
{com}.                         
.                 gen d2_disagree = . 
{txt}(1212 missing values generated)

{com}.                 replace d2_disagree = 0 if pid_3 == 1 & V06P608x >=0 & V06P608x <=2
{txt}(162 real changes made)

{com}.                 replace d2_disagree = 0 if pid_3 == 2 & V06P608x >=4 & V06P608x <=6 
{txt}(128 real changes made)

{com}.                 replace d2_disagree = 0 if pid_3 == 3 & V06P608x == 3
{txt}(5 real changes made)

{com}.                 replace d2_disagree = 1 if pid_3 == 1 & V06P608x >=4 & V06P608x <=6
{txt}(54 real changes made)

{com}.                 replace d2_disagree = 1 if pid_3 == 1 & V06P608x == 3
{txt}(3 real changes made)

{com}.                 replace d2_disagree = 1 if pid_3 == 2 & V06P608x >=0 & V06P608x <=2
{txt}(55 real changes made)

{com}.                 replace d2_disagree = 1 if pid_3 == 2 & V06P608x == 3
{txt}(3 real changes made)

{com}.                 replace d2_disagree = 1 if pid_3 == 3 & V06P608x >=0 & V06P608x <=2
{txt}(5 real changes made)

{com}.                 replace d2_disagree = 1 if pid_3 == 3 & V06P608x >=4 & V06P608x <=6
{txt}(5 real changes made)

{com}.                 label var d2_disagree "Agree with D2?"
{txt}
{com}.                 label values d2_disagree dagr
{txt}
{com}.                 
.                 gen d3_disagree = . 
{txt}(1212 missing values generated)

{com}.                 replace d3_disagree = 0 if pid_3 == 1 & V06P613x >=0 & V06P613x <=2
{txt}(134 real changes made)

{com}.                 replace d3_disagree = 0 if pid_3 == 2 & V06P613x >=4 & V06P613x <=6 
{txt}(102 real changes made)

{com}.                 replace d3_disagree = 0 if pid_3 == 3 & V06P613x == 3
{txt}(1 real change made)

{com}.                 replace d3_disagree = 1 if pid_3 == 1 & V06P613x >=4 & V06P613x <=6
{txt}(39 real changes made)

{com}.                 replace d3_disagree = 1 if pid_3 == 1 & V06P613x == 3
{txt}(2 real changes made)

{com}.                 replace d3_disagree = 1 if pid_3 == 2 & V06P613x >=0 & V06P613x <=2
{txt}(45 real changes made)

{com}.                 replace d3_disagree = 1 if pid_3 == 2 & V06P613x == 3
{txt}(4 real changes made)

{com}.                 replace d3_disagree = 1 if pid_3 == 3 & V06P613x >=0 & V06P613x <=2
{txt}(7 real changes made)

{com}.                 replace d3_disagree = 1 if pid_3 == 3 & V06P613x >=4 & V06P613x <=6
{txt}(3 real changes made)

{com}.                 label var d3_disagree "Agree with D3?"
{txt}
{com}.                 label values d3_disagree dagr
{txt}
{com}.         
. *Summary and Average
.         *Summary Agreement
.                 egen pid_agree = rowtotal(d1_agree d2_agree d3_agree), missing
{txt}(742 missing values generated)

{com}.         *Disagreement
.                 egen pid_disagree = rowtotal(d1_disagree d2_disagree d3_disagree), missing
{txt}(742 missing values generated)

{com}.         
.         *Summary Scale
.                         *See Lupton and Thornton: Exposure = D - A
.                         gen disagree_total = pid_disagree - pid_agree
{txt}(742 missing values generated)

{com}.                         label var disagree_total "Network Disagreement"
{txt}
{com}.                 *Divided by network size
.                         *See Lupton and Thonrton: (D-A)/(D+A)
.                         gen disagree_avg = [pid_disagree - pid_agree]/[pid_disagree + pid_agree]
{txt}(742 missing values generated)

{com}.                         label var disagree_avg "Network Disagreement"
{txt}
{com}.                                                 
.                 *Standardized*
.                         foreach var in disagree_total disagree_avg {c -(}
{txt}  2{com}.                                 summ `var'
{txt}  3{com}.                                 gen `var'01 = (`var' - r(min))/(r(max)-r(min))
{txt}  4{com}.                         {c )-}

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
disagree_t~l {c |}{res}       470    -1.16383    1.641925         -3          3
{txt}(742 missing values generated)

    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
disagree_avg {c |}{res}       470   -.4475177    .6375758         -1          1
{txt}(742 missing values generated)

{com}.                         
.                         label var disagree_total01 "Network Disagreement"
{txt}
{com}.                         label var disagree_avg01 "Network Disagreement"
{txt}
{com}.         
.         *Diversity Measure*
.                 *From Nir (2005): [(Agree+Disagree)/2] - |A-D|
.                         gen network_ambiv = [(pid_agree+pid_disagree)/2] - abs(pid_agree - pid_disagree)
{txt}(742 missing values generated)

{com}.                         label var network_ambiv "Network Political Diversity"
{txt}
{com}.                 
.                         summ network_ambiv

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
network_am~v {c |}{res}       470   -.4329787    .9487269       -1.5          1
{txt}
{com}.                         gen network_ambiv01=(network_ambiv - r(min))/(r(max)-r(min))
{txt}(742 missing values generated)

{com}.                         label var network_ambiv01 "Network Political Diversity"
{txt}
{com}. 
.                                 
.         
. *General Disagreement*
.         foreach var in V06P600 V06P601 V06P602 {c -(}
{txt}  2{com}.                         tab `var'
{txt}  3{com}.                         {c )-}

         {txt}Mod13_7_1. How {c |}
      different Network {c |}
        person #1 polit {c |}
        opinions from R {c |}      Freq.     Percent        Cum.
{hline 24}{c +}{hline 35}
 1. Extremely different {c |}{res}         35        7.35        7.35
{txt}      2. Very different {c |}{res}         32        6.72       14.08
{txt}3. Moderately different {c |}{res}        119       25.00       39.08
{txt}  4. Slightly different {c |}{res}        167       35.08       74.16
{txt}5. Not different at all {c |}{res}        121       25.42       99.58
{txt}          8. Don't know {c |}{res}          2        0.42      100.00
{txt}{hline 24}{c +}{hline 35}
                  Total {c |}{res}        476      100.00

         {txt}Mod13_7_2. How {c |}
      different Network {c |}
        person #2 polit {c |}
        opinions from R {c |}      Freq.     Percent        Cum.
{hline 24}{c +}{hline 35}
 1. Extremely different {c |}{res}         24        5.62        5.62
{txt}      2. Very different {c |}{res}         38        8.90       14.52
{txt}3. Moderately different {c |}{res}        105       24.59       39.11
{txt}  4. Slightly different {c |}{res}        152       35.60       74.71
{txt}5. Not different at all {c |}{res}        104       24.36       99.06
{txt}          8. Don't know {c |}{res}          4        0.94      100.00
{txt}{hline 24}{c +}{hline 35}
                  Total {c |}{res}        427      100.00

         {txt}Mod13_7_3. How {c |}
      different Network {c |}
        person #3 polit {c |}
        opinions from R {c |}      Freq.     Percent        Cum.
{hline 24}{c +}{hline 35}
 1. Extremely different {c |}{res}         16        4.66        4.66
{txt}      2. Very different {c |}{res}         37       10.79       15.45
{txt}3. Moderately different {c |}{res}         90       26.24       41.69
{txt}  4. Slightly different {c |}{res}        115       33.53       75.22
{txt}5. Not different at all {c |}{res}         79       23.03       98.25
{txt}          8. Don't know {c |}{res}          6        1.75      100.00
{txt}{hline 24}{c +}{hline 35}
                  Total {c |}{res}        343      100.00
{txt}
{com}.                 
.                 recode V06P600 (1=5) (2=4) (3=3) (4=2) (5=1) (8=.), gen(disc1_gen)
{txt}(357 differences between V06P600 and disc1_gen)

{com}.                 recode V06P601 (1=5) (2=4) (3=3) (4=2) (5=1) (8=.), gen(disc2_gen)
{txt}(322 differences between V06P601 and disc2_gen)

{com}.                 recode V06P602 (1=5) (2=4) (3=3) (4=2) (5=1) (8=.), gen(disc3_gen)
{txt}(253 differences between V06P602 and disc3_gen)

{com}.                 label def gagree 1 "Not At All" 2 "Slightly Different" 3 "Moderately Different" 4 "Very Different" 5 "Extremely Different"
{txt}
{com}.                 
.                 foreach var in disc1_gen disc2_gen disc3_gen {c -(}
{txt}  2{com}.                         label values `var' gagree
{txt}  3{com}.                         tab `var'
{txt}  4{com}.                         summ `var'
{txt}  5{com}.                         {c )-}

   {txt}RECODE of V06P600 {c |}
     (Mod13_7_1. How {c |}
   different Network {c |}
     person #1 polit {c |}
        opinions fro {c |}      Freq.     Percent        Cum.
{hline 21}{c +}{hline 35}
          Not At All {c |}{res}        121       25.53       25.53
{txt}  Slightly Different {c |}{res}        167       35.23       60.76
{txt}Moderately Different {c |}{res}        119       25.11       85.86
{txt}      Very Different {c |}{res}         32        6.75       92.62
{txt} Extremely Different {c |}{res}         35        7.38      100.00
{txt}{hline 21}{c +}{hline 35}
               Total {c |}{res}        474      100.00

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 3}disc1_gen {c |}{res}       474    2.352321    1.148731          1          5

   {txt}RECODE of V06P601 {c |}
     (Mod13_7_2. How {c |}
   different Network {c |}
     person #2 polit {c |}
        opinions fro {c |}      Freq.     Percent        Cum.
{hline 21}{c +}{hline 35}
          Not At All {c |}{res}        104       24.59       24.59
{txt}  Slightly Different {c |}{res}        152       35.93       60.52
{txt}Moderately Different {c |}{res}        105       24.82       85.34
{txt}      Very Different {c |}{res}         38        8.98       94.33
{txt} Extremely Different {c |}{res}         24        5.67      100.00
{txt}{hline 21}{c +}{hline 35}
               Total {c |}{res}        423      100.00

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 3}disc2_gen {c |}{res}       423    2.352246    1.114867          1          5

   {txt}RECODE of V06P602 {c |}
     (Mod13_7_3. How {c |}
   different Network {c |}
     person #3 polit {c |}
        opinions fro {c |}      Freq.     Percent        Cum.
{hline 21}{c +}{hline 35}
          Not At All {c |}{res}         79       23.44       23.44
{txt}  Slightly Different {c |}{res}        115       34.12       57.57
{txt}Moderately Different {c |}{res}         90       26.71       84.27
{txt}      Very Different {c |}{res}         37       10.98       95.25
{txt} Extremely Different {c |}{res}         16        4.75      100.00
{txt}{hline 21}{c +}{hline 35}
               Total {c |}{res}        337      100.00

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 3}disc3_gen {c |}{res}       337    2.394659    1.102635          1          5
{txt}
{com}.                         
.                 egen disc_gen = rowmean(disc1_gen disc2_gen disc3_gen)
{txt}(737 missing values generated)

{com}.                 label var disc_gen "Avg. General Disagreement"
{txt}
{com}.                 summ disc_gen

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 4}disc_gen {c |}{res}       475    2.380702    .8351819          1          5
{txt}
{com}. 
. 
.         *Relationship wtih PID Total
.                 pwcorr disagree_total disagree_avg disc_gen, sig

             {txt}{c |} disagr~l disagr~g disc_gen
{hline 13}{c +}{hline 27}
disagree_t~l {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
disagree_avg {c |} {res}  0.9319   1.0000 
             {txt}{c |}{res}   0.0000
             {txt}{c |}
    disc_gen {c |} {res}  0.4484   0.4564   1.0000 
             {txt}{c |}{res}   0.0000   0.0000
             {txt}{c |}

{com}.                 pwcorr d1_disagree disc1_gen d2_disagree disc2_gen d3_disagree disc3_gen, sig

             {txt}{c |} d1_dis~e disc1_~n d2_dis~e disc2_~n d3_dis~e disc3_~n
{hline 13}{c +}{hline 54}
 d1_disagree {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
   disc1_gen {c |} {res}  0.4508   1.0000 
             {txt}{c |}{res}   0.0000
             {txt}{c |}
 d2_disagree {c |} {res}  0.1744   0.0886   1.0000 
             {txt}{c |}{res}   0.0003   0.0699
             {txt}{c |}
   disc2_gen {c |} {res}  0.1224   0.2182   0.4922   1.0000 
             {txt}{c |}{res}   0.0125   0.0000   0.0000
             {txt}{c |}
 d3_disagree {c |} {res}  0.1460  -0.0310   0.1020   0.0304   1.0000 
             {txt}{c |}{res}   0.0076   0.5714   0.0623   0.5800
             {txt}{c |}
   disc3_gen {c |} {res}  0.1803   0.1431   0.0987   0.2010   0.4926   1.0000 
             {txt}{c |}{res}   0.0010   0.0086   0.0728   0.0002   0.0000
             {txt}{c |}

{com}.                 tab d1_disagree disc1_gen, row col chi2
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

            {c |}  RECODE of V06P600 (Mod13_7_1. How different Network
   Disagree {c |}              person #1 polit opinions fro
   with D1? {c |} Not At Al  Slightly   Moderatel  Very Diff  Extremely {c |}     Total
{hline 12}{c +}{hline 55}{c +}{hline 10}
  PID Agree {c |}{res}       113        139         80         10         10 {txt}{c |}{res}       352 
            {txt}{c |}{res}     32.10      39.49      22.73       2.84       2.84 {txt}{c |}{res}    100.00 
            {txt}{c |}{res}     93.39      84.76      70.18      31.25      29.41 {txt}{c |}{res}     75.70 
{txt}{hline 12}{c +}{hline 55}{c +}{hline 10}
PID Disgree {c |}{res}         8         25         34         22         24 {txt}{c |}{res}       113 
            {txt}{c |}{res}      7.08      22.12      30.09      19.47      21.24 {txt}{c |}{res}    100.00 
            {txt}{c |}{res}      6.61      15.24      29.82      68.75      70.59 {txt}{c |}{res}     24.30 
{txt}{hline 12}{c +}{hline 55}{c +}{hline 10}
      Total {c |}{res}       121        164        114         32         34 {txt}{c |}{res}       465 
            {txt}{c |}{res}     26.02      35.27      24.52       6.88       7.31 {txt}{c |}{res}    100.00 
            {txt}{c |}{res}    100.00     100.00     100.00     100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}4{txt}) = {res}103.7539  {txt} Pr = {res}0.000
{txt}
{com}.                 tab d2_disagree disc2_gen, row col chi2
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

            {c |}  RECODE of V06P601 (Mod13_7_2. How different Network
 Agree with {c |}              person #2 polit opinions fro
        D2? {c |} Not At Al  Slightly   Moderatel  Very Diff  Extremely {c |}     Total
{hline 12}{c +}{hline 55}{c +}{hline 10}
  PID Agree {c |}{res}        98        120         61          9          6 {txt}{c |}{res}       294 
            {txt}{c |}{res}     33.33      40.82      20.75       3.06       2.04 {txt}{c |}{res}    100.00 
            {txt}{c |}{res}     95.15      80.00      59.80      23.68      25.00 {txt}{c |}{res}     70.50 
{txt}{hline 12}{c +}{hline 55}{c +}{hline 10}
PID Disgree {c |}{res}         5         30         41         29         18 {txt}{c |}{res}       123 
            {txt}{c |}{res}      4.07      24.39      33.33      23.58      14.63 {txt}{c |}{res}    100.00 
            {txt}{c |}{res}      4.85      20.00      40.20      76.32      75.00 {txt}{c |}{res}     29.50 
{txt}{hline 12}{c +}{hline 55}{c +}{hline 10}
      Total {c |}{res}       103        150        102         38         24 {txt}{c |}{res}       417 
            {txt}{c |}{res}     24.70      35.97      24.46       9.11       5.76 {txt}{c |}{res}    100.00 
            {txt}{c |}{res}    100.00     100.00     100.00     100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}4{txt}) = {res}106.1458  {txt} Pr = {res}0.000
{txt}
{com}.                 tab d3_disagree disc3_gen, row col chi2
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

            {c |}  RECODE of V06P602 (Mod13_7_3. How different Network
 Agree with {c |}              person #3 polit opinions fro
        D3? {c |} Not At Al  Slightly   Moderatel  Very Diff  Extremely {c |}     Total
{hline 12}{c +}{hline 55}{c +}{hline 10}
  PID Agree {c |}{res}        75         94         48         14          3 {txt}{c |}{res}       234 
            {txt}{c |}{res}     32.05      40.17      20.51       5.98       1.28 {txt}{c |}{res}    100.00 
            {txt}{c |}{res}     96.15      82.46      54.55      37.84      18.75 {txt}{c |}{res}     70.27 
{txt}{hline 12}{c +}{hline 55}{c +}{hline 10}
PID Disgree {c |}{res}         3         20         40         23         13 {txt}{c |}{res}        99 
            {txt}{c |}{res}      3.03      20.20      40.40      23.23      13.13 {txt}{c |}{res}    100.00 
            {txt}{c |}{res}      3.85      17.54      45.45      62.16      81.25 {txt}{c |}{res}     29.73 
{txt}{hline 12}{c +}{hline 55}{c +}{hline 10}
      Total {c |}{res}        78        114         88         37         16 {txt}{c |}{res}       333 
            {txt}{c |}{res}     23.42      34.23      26.43      11.11       4.80 {txt}{c |}{res}    100.00 
            {txt}{c |}{res}    100.00     100.00     100.00     100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}4{txt}) = {res} 82.4911  {txt} Pr = {res}0.000
{txt}
{com}.                 ttest disc1_gen, by(d1_disagree)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
PID Agre {c |}{res}{col 12}    352{col 22} 2.048295{col 34}  .051065{col 46} .9580651{col 58} 1.947864{col 70} 2.148727
{txt}PID Disg {c |}{res}{col 12}    113{col 22} 3.256637{col 34} .1150625{col 46} 1.223131{col 58} 3.028656{col 70} 3.484619
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}    465{col 22} 2.341935{col 34} .0533731{col 46} 1.150929{col 58} 2.237053{col 70} 2.446818
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}-1.208342{col 34} .1112005{col 58}-1.426862{col 70}-.9898216
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}PID Agre{txt}) - mean({res}PID Disg{txt})                        t = {res}-10.8663
{txt}Ho: diff = 0                                     degrees of freedom = {res}     463

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}1.0000
{txt}
{com}.                 ttest disc2_gen, by(d2_disagree)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
PID Agre {c |}{res}{col 12}    294{col 22} 1.996599{col 34} .0537637{col 46} .9218556{col 58} 1.890787{col 70} 2.102411
{txt}PID Disg {c |}{res}{col 12}    123{col 22} 3.203252{col 34} .0986105{col 46} 1.093643{col 58} 3.008043{col 70} 3.398461
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}    417{col 22} 2.352518{col 34} .0548124{col 46} 1.119302{col 58} 2.244774{col 70} 2.460262
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}-1.206653{col 34} .1047538{col 58}-1.412568{col 70}-1.000739
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}PID Agre{txt}) - mean({res}PID Disg{txt})                        t = {res}-11.5189
{txt}Ho: diff = 0                                     degrees of freedom = {res}     415

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}1.0000
{txt}
{com}.                 ttest disc3_gen, by(d3_disagree)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
PID Agre {c |}{res}{col 12}    234{col 22} 2.042735{col 34}  .061404{col 46} .9393008{col 58} 1.921757{col 70} 2.163713
{txt}PID Disg {c |}{res}{col 12}     99{col 22} 3.232323{col 34} .1023616{col 46} 1.018485{col 58}  3.02919{col 70} 3.435457
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}    333{col 22} 2.396396{col 34} .0605757{col 46} 1.105402{col 58} 2.277236{col 70} 2.515557
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}-1.189588{col 34} .1155085{col 58}-1.416811{col 70}-.9623649
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}PID Agre{txt}) - mean({res}PID Disg{txt})                        t = {res}-10.2987
{txt}Ho: diff = 0                                     degrees of freedom = {res}     331

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}1.0000
{txt}
{com}.                 
.                 
.         *Combined Index (Old)
.                 gen d1_both = d1_disagree + disc1_gen
{txt}(747 missing values generated)

{com}.                 gen d2_both = d2_disagree + disc2_gen
{txt}(795 missing values generated)

{com}.                 gen d3_both = d3_disagree + disc3_gen
{txt}(879 missing values generated)

{com}.                 tab d1_both

    {txt}d1_both {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}        113       24.30       24.30
{txt}          2 {c |}{res}        147       31.61       55.91
{txt}          3 {c |}{res}        105       22.58       78.49
{txt}          4 {c |}{res}         44        9.46       87.96
{txt}          5 {c |}{res}         32        6.88       94.84
{txt}          6 {c |}{res}         24        5.16      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        465      100.00
{txt}
{com}.                 tab d2_both

    {txt}d2_both {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}         98       23.50       23.50
{txt}          2 {c |}{res}        125       29.98       53.48
{txt}          3 {c |}{res}         91       21.82       75.30
{txt}          4 {c |}{res}         50       11.99       87.29
{txt}          5 {c |}{res}         35        8.39       95.68
{txt}          6 {c |}{res}         18        4.32      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        417      100.00
{txt}
{com}.                 tab d3_both

    {txt}d3_both {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}         75       22.52       22.52
{txt}          2 {c |}{res}         97       29.13       51.65
{txt}          3 {c |}{res}         68       20.42       72.07
{txt}          4 {c |}{res}         54       16.22       88.29
{txt}          5 {c |}{res}         26        7.81       96.10
{txt}          6 {c |}{res}         13        3.90      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        333      100.00
{txt}
{com}.                 
.                 egen dis_both = rowmean(d1_both d2_both d3_both)
{txt}(742 missing values generated)

{com}.                 label var dis_both "Index of Network Disagreement"
{txt}
{com}.                 summ dis_both, detail

                {txt}Index of Network Disagreement
{hline 61}
      Percentiles      Smallest
 1%    {res}        1              1
{txt} 5%    {res}        1              1
{txt}10%    {res} 1.333333              1       {txt}Obs         {res}        470
{txt}25%    {res}        2              1       {txt}Sum of Wgt. {res}        470

{txt}50%    {res} 2.666667                      {txt}Mean          {res} 2.658511
                        {txt}Largest       Std. Dev.     {res} 1.023471
{txt}75%    {res} 3.333333              6
{txt}90%    {res}        4              6       {txt}Variance      {res} 1.047493
{txt}95%    {res} 4.333333              6       {txt}Skewness      {res} .5780931
{txt}99%    {res}        6              6       {txt}Kurtosis      {res} 3.409491
{txt}
{com}.                 tab dis_both

   {txt}Index of {c |}
    Network {c |}
Disagreemen {c |}
          t {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}         34        7.23        7.23
{txt}   1.333333 {c |}{res}         22        4.68       11.91
{txt}        1.5 {c |}{res}          9        1.91       13.83
{txt}   1.666667 {c |}{res}         31        6.60       20.43
{txt}          2 {c |}{res}         72       15.32       35.74
{txt}   2.333333 {c |}{res}         40        8.51       44.26
{txt}        2.5 {c |}{res}         13        2.77       47.02
{txt}   2.666667 {c |}{res}         53       11.28       58.30
{txt}          3 {c |}{res}         71       15.11       73.40
{txt}   3.333333 {c |}{res}         31        6.60       80.00
{txt}        3.5 {c |}{res}         10        2.13       82.13
{txt}   3.666667 {c |}{res}         20        4.26       86.38
{txt}          4 {c |}{res}         30        6.38       92.77
{txt}   4.333333 {c |}{res}         11        2.34       95.11
{txt}        4.5 {c |}{res}          6        1.28       96.38
{txt}   4.666667 {c |}{res}          4        0.85       97.23
{txt}          5 {c |}{res}          5        1.06       98.30
{txt}   5.333333 {c |}{res}          1        0.21       98.51
{txt}        5.5 {c |}{res}          1        0.21       98.72
{txt}          6 {c |}{res}          6        1.28      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        470      100.00
{txt}
{com}.                 
.         *Combined Index (new)
.         /**fn. 5 (Lupton and Thornton: 
>                         A = sum(ai * si) where a = 1 if agree and s = agreement weight
>                         D = sum(di * si) where d = 1 if disagree and s = disagreement weight
>                         Disagreement = D - A**/
.                         
.                 *Agreement Coding*
.                         foreach var in disc1_gen disc2_gen disc3_gen {c -(}
{txt}  2{com}.                                 recode `var' (1=5) (2=4) (3=3) (4=2) (5=1), gen(`var'_ag)
{txt}  3{com}.                                 {c )-}
{txt}(355 differences between disc1_gen and disc1_gen_ag)
(318 differences between disc2_gen and disc2_gen_ag)
(247 differences between disc3_gen and disc3_gen_ag)

{com}.                 *Agree Scale
.                         gen a1 = d1_agree * disc1_gen_ag
{txt}(747 missing values generated)

{com}.                         gen a2 = d2_agree * disc2_gen_ag
{txt}(795 missing values generated)

{com}.                         gen a3 = d3_agree * disc3_gen_ag
{txt}(879 missing values generated)

{com}.                         egen agree_weight = rowtotal(a1 a2 a3), missing
{txt}(742 missing values generated)

{com}. 
.                 
.                 *Disagree Scale
.                         gen d1 = d1_disagree * disc1_gen
{txt}(747 missing values generated)

{com}.                         gen d2 = d2_disagree * disc2_gen
{txt}(795 missing values generated)

{com}.                         gen d3 = d3_disagree * disc3_gen
{txt}(879 missing values generated)

{com}.                         egen disagree_weight = rowtotal(d1 d2 d3), missing
{txt}(742 missing values generated)

{com}. 
.                 *Exposure to Disagreement
.                         gen disagree_total_weight = disagree_weight - agree_weight
{txt}(742 missing values generated)

{com}.                         gen disagree_avg_weight = disagree_total_weight/(pid_disagree+pid_agree)
{txt}(742 missing values generated)

{com}.                 
.                         foreach var in disagree_total_weight disagree_avg_weight {c -(}
{txt}  2{com}.                                 summ `var' 
{txt}  3{com}.                                 gen `var'01 = (`var' - r(min))/(r(max)-r(min))
{txt}  4{com}.                                 tab `var'01
{txt}  5{com}.                         {c )-}

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
disagree_t~t {c |}{res}       470   -5.131915     6.27613        -15         15
{txt}(742 missing values generated)

disagree_to {c |}
tal_weight0 {c |}
          1 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}         21        4.47        4.47
{txt}   .0333333 {c |}{res}         21        4.47        8.94
{txt}   .0666667 {c |}{res}         27        5.74       14.68
{txt}         .1 {c |}{res}         25        5.32       20.00
{txt}   .1333333 {c |}{res}         24        5.11       25.11
{txt}   .1666667 {c |}{res}         23        4.89       30.00
{txt}         .2 {c |}{res}         16        3.40       33.40
{txt}   .2333333 {c |}{res}         21        4.47       37.87
{txt}   .2666667 {c |}{res}         17        3.62       41.49
{txt}         .3 {c |}{res}         23        4.89       46.38
{txt}   .3333333 {c |}{res}         36        7.66       54.04
{txt}   .3666667 {c |}{res}         42        8.94       62.98
{txt}         .4 {c |}{res}         33        7.02       70.00
{txt}   .4333333 {c |}{res}         13        2.77       72.77
{txt}   .4666667 {c |}{res}         16        3.40       76.17
{txt}         .5 {c |}{res}         18        3.83       80.00
{txt}   .5333334 {c |}{res}         12        2.55       82.55
{txt}   .5666667 {c |}{res}         18        3.83       86.38
{txt}         .6 {c |}{res}         12        2.55       88.94
{txt}   .6333333 {c |}{res}         15        3.19       92.13
{txt}   .6666667 {c |}{res}         14        2.98       95.11
{txt}         .7 {c |}{res}          6        1.28       96.38
{txt}   .7333333 {c |}{res}          6        1.28       97.66
{txt}   .7666667 {c |}{res}          4        0.85       98.51
{txt}         .8 {c |}{res}          3        0.64       99.15
{txt}   .8333333 {c |}{res}          1        0.21       99.36
{txt}   .8666667 {c |}{res}          1        0.21       99.57
{txt}   .9333333 {c |}{res}          1        0.21       99.79
{txt}          1 {c |}{res}          1        0.21      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        470      100.00

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
disagree_a~t {c |}{res}       470   -1.952482    2.412086         -5          5
{txt}(742 missing values generated)

disagree_av {c |}
 g_weight01 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}         34        7.23        7.23
{txt}   .0333333 {c |}{res}         21        4.47       11.70
{txt}        .05 {c |}{res}          8        1.70       13.40
{txt}   .0666666 {c |}{res}         27        5.74       19.15
{txt}         .1 {c |}{res}         58       12.34       31.49
{txt}   .1333333 {c |}{res}         24        5.11       36.60
{txt}        .15 {c |}{res}          6        1.28       37.87
{txt}   .1666667 {c |}{res}         16        3.40       41.28
{txt}         .2 {c |}{res}         26        5.53       46.81
{txt}   .2333333 {c |}{res}          5        1.06       47.87
{txt}        .25 {c |}{res}          1        0.21       48.09
{txt}   .2666667 {c |}{res}         11        2.34       50.43
{txt}         .3 {c |}{res}         17        3.62       54.04
{txt}   .3333333 {c |}{res}         29        6.17       60.21
{txt}        .35 {c |}{res}          2        0.43       60.64
{txt}   .3666667 {c |}{res}         24        5.11       65.74
{txt}         .4 {c |}{res}         29        6.17       71.91
{txt}   .4333333 {c |}{res}          6        1.28       73.19
{txt}        .45 {c |}{res}         10        2.13       75.32
{txt}   .4666667 {c |}{res}          4        0.85       76.17
{txt}         .5 {c |}{res}         18        3.83       80.00
{txt}   .5333334 {c |}{res}          6        1.28       81.28
{txt}        .55 {c |}{res}          5        1.06       82.34
{txt}   .5666667 {c |}{res}         11        2.34       84.68
{txt}         .6 {c |}{res}         14        2.98       87.66
{txt}   .6333333 {c |}{res}         13        2.77       90.43
{txt}   .6666667 {c |}{res}          8        1.70       92.13
{txt}         .7 {c |}{res}          8        1.70       93.83
{txt}   .7333333 {c |}{res}          3        0.64       94.47
{txt}        .75 {c |}{res}          1        0.21       94.68
{txt}   .7666667 {c |}{res}          3        0.64       95.32
{txt}         .8 {c |}{res}          8        1.70       97.02
{txt}   .8333333 {c |}{res}          1        0.21       97.23
{txt}        .85 {c |}{res}          3        0.64       97.87
{txt}   .8666667 {c |}{res}          1        0.21       98.09
{txt}         .9 {c |}{res}          2        0.43       98.51
{txt}   .9333333 {c |}{res}          1        0.21       98.72
{txt}          1 {c |}{res}          6        1.28      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        470      100.00
{txt}
{com}.                         
.                         label var disagree_total_weight "Network Disagreement"
{txt}
{com}.                         label var disagree_total_weight "Network Disagreement"
{txt}
{com}.                 
.                         label var disagree_avg_weight "Network Disagreement"
{txt}
{com}.                         label var disagree_avg_weight "Network Disagreement"
{txt}
{com}.                 
. 
. 
. *Netowrk Disagreemetn (Klofstad)
.         foreach var in V06P603x V06P608x V06P613x {c -(}
{txt}  2{com}.                 tab `var'
{txt}  3{com}.                 {c )-}

  {txt}Mod13_8_1x. SUMMARY: Party ID network {c |}
                             mention #1 {c |}      Freq.     Percent        Cum.
{hline 40}{c +}{hline 35}
             0. Strong Democrat (1/1/.) {c |}{res}        139       29.20       29.20
{txt}           1. Weak Democrat (1/2-8-9/.) {c |}{res}         58       12.18       41.39
{txt}  2. Independent-Democrat (3-4-5-8/./1) {c |}{res}         49       10.29       51.68
{txt}3. Independent-Independent (3-5/./5-8-9 {c |}{res}         14        2.94       54.62
{txt}4. Independent-Republican (3-4-5-8/./3) {c |}{res}         34        7.14       61.76
{txt}         5. Weak Republican (2/2-8-9/.) {c |}{res}         52       10.92       72.69
{txt}           6. Strong Republican (2/1/.) {c |}{res}        124       26.05       98.74
{txt}                   7. Other (4/./2-8-9) {c |}{res}          2        0.42       99.16
{txt}                8. Don't know (8/./8-9) {c |}{res}          4        0.84      100.00
{txt}{hline 40}{c +}{hline 35}
                                  Total {c |}{res}        476      100.00

  {txt}Mod13_8_2x. SUMMARY: Party ID network {c |}
                             mention #2 {c |}      Freq.     Percent        Cum.
{hline 40}{c +}{hline 35}
             0. Strong Democrat (1/1/.) {c |}{res}        126       29.51       29.51
{txt}           1. Weak Democrat (1/2-8-9/.) {c |}{res}         65       15.22       44.73
{txt}  2. Independent-Democrat (3-4-5-8/./1) {c |}{res}         32        7.49       52.22
{txt}3. Independent-Independent (3-5/./5-8-9 {c |}{res}         11        2.58       54.80
{txt}4. Independent-Republican (3-4-5-8/./3) {c |}{res}         28        6.56       61.36
{txt}         5. Weak Republican (2/2-8-9/.) {c |}{res}         57       13.35       74.71
{txt}           6. Strong Republican (2/1/.) {c |}{res}        102       23.89       98.59
{txt}                   7. Other (4/./2-8-9) {c |}{res}          1        0.23       98.83
{txt}                8. Don't know (8/./8-9) {c |}{res}          5        1.17      100.00
{txt}{hline 40}{c +}{hline 35}
                                  Total {c |}{res}        427      100.00

  {txt}Mod13_8_3x. SUMMARY: Party ID network {c |}
                             mention #3 {c |}      Freq.     Percent        Cum.
{hline 40}{c +}{hline 35}
             0. Strong Democrat (1/1/.) {c |}{res}         94       27.41       27.41
{txt}           1. Weak Democrat (1/2-8-9/.) {c |}{res}         55       16.03       43.44
{txt}  2. Independent-Democrat (3-4-5-8/./1) {c |}{res}         38       11.08       54.52
{txt}3. Independent-Independent (3-5/./5-8-9 {c |}{res}          7        2.04       56.56
{txt}4. Independent-Republican (3-4-5-8/./3) {c |}{res}         19        5.54       62.10
{txt}         5. Weak Republican (2/2-8-9/.) {c |}{res}         41       11.95       74.05
{txt}           6. Strong Republican (2/1/.) {c |}{res}         84       24.49       98.54
{txt}                8. Don't know (8/./8-9) {c |}{res}          5        1.46      100.00
{txt}{hline 40}{c +}{hline 35}
                                  Total {c |}{res}        343      100.00
{txt}
{com}.                 *0-2: Democrat
.                 *3: Independent
.                 *4-6: Republican
.                 
.         recode V06P603x (0=1) (1=2) (2=3) (3=4) (4=5) (5=6) (6=7) (7=.) (9=.), gen(partyid_d1)
{txt}(472 differences between V06P603x and partyid_d1)

{com}.         recode V06P608x (0=1) (1=2) (2=3) (3=4) (4=5) (5=6) (6=7) (7=.) (9=.), gen(partyid_d2)
{txt}(422 differences between V06P608x and partyid_d2)

{com}.         recode V06P613x (0=1) (1=2) (2=3) (3=4) (4=5) (5=6) (6=7) (7=.) (9=.), gen(partyid_d3)
{txt}(338 differences between V06P613x and partyid_d3)

{com}.         label values partyid_d1 pi
{txt}
{com}.         label values partyid_d2 pi
{txt}
{com}.         label values partyid_d3 pi
{txt}
{com}.         
.         gen pdiff_d1 = abs(partyid - partyid_d1)
{txt}(742 missing values generated)

{com}.         gen pdiff_d2 = abs(partyid - partyid_d2)
{txt}(787 missing values generated)

{com}.         gen pdiff_d3 = abs(partyid - partyid_d3)
{txt}(870 missing values generated)

{com}.         egen pdiff_avg = rowmean(pdiff_d1 pdiff_d1 pdiff_d3)
{txt}(740 missing values generated)

{com}.         label var pdiff_avg "Avg. Disagreement in PID Scale"
{txt}
{com}.         
.         
.         
.         
.         
.         
.                 ***************************************************
.                 *****************Control Variables*****************
.                 ***************************************************
.                 
. *Education
.         recode  V043254 (0=1) (1=1) (2=1) (3=2) (4=3) (5=3) (6=4) (7=4), gen(educ)
{txt}(1175 differences between V043254 and educ)

{com}.         label var educ "Education"
{txt}
{com}.         label def edu 1 "< HS" 2 "HS" 3 "Some College" 4 "College Degree+"
{txt}
{com}.         label values educ edu
{txt}
{com}.         tab educ

      {txt}Education {c |}      Freq.     Percent        Cum.
{hline 16}{c +}{hline 35}
           < HS {c |}{res}        111        9.16        9.16
{txt}             HS {c |}{res}        355       29.29       38.45
{txt}   Some College {c |}{res}        384       31.68       70.13
{txt}College Degree+ {c |}{res}        362       29.87      100.00
{txt}{hline 16}{c +}{hline 35}
          Total {c |}{res}      1,212      100.00
{txt}
{com}.         
.         summ educ 

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 8}educ {c |}{res}      1212    2.822607    .9629214          1          4
{txt}
{com}.         gen educ01 = (educ - r(min))/(r(max)-r(min))
{txt}
{com}.         label var educ01 "Education"
{txt}
{com}. 
. 
. 
. *Gender
.         recode V06P005 (2=1) (1=0), gen(gender)
{txt}(675 differences between V06P005 and gender)

{com}.         label def gend 1 "Female" 0 "Male"
{txt}
{com}.         label values gender gend
{txt}
{com}.         label var gender gend
{txt}
{com}.         
. 
. *Age
.         rename V06P006 age
{res}{txt}
{com}.         summ age 

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 9}age {c |}{res}       675     51.5363    16.05664         20         92
{txt}
{com}.         
.         summ age

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 9}age {c |}{res}       675     51.5363    16.05664         20         92
{txt}
{com}.         gen age01 = (age - r(min))/(r(max)-r(min))
{txt}(537 missing values generated)

{com}.         label var age01 "Age"
{txt}
{com}.         
.         
. 
. *Race/Hisp
.         gen race = . 
{txt}(1212 missing values generated)

{com}.         replace race = 1 if V043299 == 50
{txt}(876 real changes made)

{com}.         replace race = 1 if V043299 == 45
{txt}(4 real changes made)

{com}.         replace race = 2 if V043299 >= 10 & V043299  <= 15
{txt}(184 real changes made)

{com}.         replace race = 3 if V043299 >= 20 & V043299  <= 40
{txt}(133 real changes made)

{com}.         replace race = 3 if V043299 == 70
{txt}(7 real changes made)

{com}.         tab V043299  race

  {txt}Y24x. SUMMARY: Race {c |}               race
        of Respondent {c |}         1          2          3 {c |}     Total
{hline 22}{c +}{hline 33}{c +}{hline 10}
            10. Black {c |}{res}         0        180          0 {txt}{c |}{res}       180 
{txt}14. Black and Hispani {c |}{res}         0          2          0 {txt}{c |}{res}         2 
{txt}  15. Black and White {c |}{res}         0          2          0 {txt}{c |}{res}         2 
{txt}            20. Asian {c |}{res}         0          0         28 {txt}{c |}{res}        28 
{txt}23. Asian and Native  {c |}{res}         0          0          1 {txt}{c |}{res}         1 
{txt}  25. Asian and White {c |}{res}         0          0          4 {txt}{c |}{res}         4 
{txt}  30. Native American {c |}{res}         0          0         12 {txt}{c |}{res}        12 
{txt}35. Native American a {c |}{res}         0          0          7 {txt}{c |}{res}         7 
{txt}         40. Hispanic {c |}{res}         0          0         81 {txt}{c |}{res}        81 
{txt}45. Hispanic and Whit {c |}{res}         4          0          0 {txt}{c |}{res}         4 
{txt}50. White (no mention {c |}{res}       876          0          0 {txt}{c |}{res}       876 
{txt}            70. Other {c |}{res}         0          0          7 {txt}{c |}{res}         7 
{txt}{hline 22}{c +}{hline 33}{c +}{hline 10}
                Total {c |}{res}       880        184        140 {txt}{c |}{res}     1,204 

{txt}
{com}.         label var race "Race"
{txt}
{com}.         label def rac 1 "White" 2 "Black" 3 "Other"
{txt}
{com}.         label values race rac
{txt}
{com}.         
.         
. *Marital Status
.         recode  V043251 (1=1) (2=0) (3=0) (4=0) (5=0) (6=0) (8=.), gen(marital)
{txt}(587 differences between V043251 and marital)

{com}.         label def mar 1 "Married" 0 "Not Married" 
{txt}
{com}.         label values marital mar
{txt}
{com}.         label var marital "2004 Marital Status"
{txt}
{com}.         tab marital

       {txt}2004 {c |}
    Marital {c |}
     Status {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
Not Married {c |}{res}        586       48.39       48.39
{txt}    Married {c |}{res}        625       51.61      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,211      100.00
{txt}
{com}.         
. *Employment
.         recode  V043260c (1=1) (2=2) (4=2) (5=3) (6=4) (7=5) (8=6) (9=.), gen(employment)
{txt}(409 differences between V043260c and employment)

{com}.         label var employment "2004 Employment Status" 
{txt}
{com}.         label def emp 1 "Employed" 2 "Unemployed" 3 "Retired" 4 "Perm. Disabled" 5 "Homemaker" 6 "Student"
{txt}
{com}.         label values employment emp
{txt}
{com}.         tab employment if  V06P004 !=.

          {txt}2004 {c |}
    Employment {c |}
        Status {c |}      Freq.     Percent        Cum.
{hline 15}{c +}{hline 35}
      Employed {c |}{res}        441       65.33       65.33
{txt}    Unemployed {c |}{res}         21        3.11       68.44
{txt}       Retired {c |}{res}        135       20.00       88.44
{txt}Perm. Disabled {c |}{res}         22        3.26       91.70
{txt}     Homemaker {c |}{res}         43        6.37       98.07
{txt}       Student {c |}{res}         13        1.93      100.00
{txt}{hline 15}{c +}{hline 35}
         Total {c |}{res}        675      100.00
{txt}
{com}.         
.         recode employment (1=1) (2=2) (3=3) (4=4) (5=4) (6=4), gen(employed)
{txt}(118 differences between employment and employed)

{com}.         label var employed "2004 Employment Status" 
{txt}
{com}.         label def emp1 1 "Employed" 2 "Unemployed" 3 "Retired" 4 "Disabled/Homemaker/Student"
{txt}
{com}.         label values employed emp1
{txt}
{com}.         
. 
. *Income
.         tab V043293x

{txt}Y21a. Summary: Household income {c |}      Freq.     Percent        Cum.
{hline 32}{c +}{hline 35}
            00. NA (8,9 in Y20) {c |}{res}         24        1.98        1.98
{txt}01. A. None or less than $2,999 {c |}{res}         23        1.90        3.88
{txt}          02. B. $3,000 -$4,999 {c |}{res}         17        1.40        5.28
{txt}          03. C. $5,000 -$6,999 {c |}{res}         17        1.40        6.68
{txt}          04. D. $7,000 -$8,999 {c |}{res}         22        1.82        8.50
{txt}         05. E. $9,000 -$10,999 {c |}{res}         27        2.23       10.73
{txt}         06. F. $11,000-$12,999 {c |}{res}         26        2.15       12.87
{txt}         07. G. $13,000-$14,999 {c |}{res}         24        1.98       14.85
{txt}         08. H. $15,000-$16,999 {c |}{res}         28        2.31       17.16
{txt}         09. J. $17,000-$19,999 {c |}{res}         21        1.73       18.89
{txt}         10. K. $20,000-$21,999 {c |}{res}         32        2.64       21.53
{txt}         11. M. $22,000-$24,999 {c |}{res}         47        3.88       25.41
{txt}         12. N. $25,000-$29,999 {c |}{res}         51        4.21       29.62
{txt}         13. P. $30,000-$34,999 {c |}{res}         58        4.79       34.41
{txt}         14. Q. $35,000-$39,999 {c |}{res}         48        3.96       38.37
{txt}         15. R. $40,000-$44,999 {c |}{res}         66        5.45       43.81
{txt}         16. S. $45,000-$49,999 {c |}{res}         49        4.04       47.85
{txt}         17. T. $50,000-$59,999 {c |}{res}         83        6.85       54.70
{txt}         18. U. $60,000-$69,999 {c |}{res}         81        6.68       61.39
{txt}         19. V. $70,000-$79,999 {c |}{res}         77        6.35       67.74
{txt}         20. W. $80,000-$89,999 {c |}{res}         63        5.20       72.94
{txt}        21. X. $90,000-$104,999 {c |}{res}         55        4.54       77.48
{txt}       22. Y. $105,000-$119,000 {c |}{res}         38        3.14       80.61
{txt}       23. Z. $120,000 and over {c |}{res}        117        9.65       90.26
{txt}                 88. Don't know {c |}{res}         38        3.14       93.40
{txt}                    89. Refused {c |}{res}         80        6.60      100.00
{txt}{hline 32}{c +}{hline 35}
                          Total {c |}{res}      1,212      100.00
{txt}
{com}.         rename V043293x income
{res}{txt}
{com}.         label var income "2004 Household Income"
{txt}
{com}.         mvdecode income, mv(00 = .a \ 88 = .b \ 89 = .c)
      {txt}income:{res}{col 15}142{txt} missing values generated

{com}.         tab income

          {txt}2004 Household Income {c |}      Freq.     Percent        Cum.
{hline 32}{c +}{hline 35}
01. A. None or less than $2,999 {c |}{res}         23        2.15        2.15
{txt}          02. B. $3,000 -$4,999 {c |}{res}         17        1.59        3.74
{txt}          03. C. $5,000 -$6,999 {c |}{res}         17        1.59        5.33
{txt}          04. D. $7,000 -$8,999 {c |}{res}         22        2.06        7.38
{txt}         05. E. $9,000 -$10,999 {c |}{res}         27        2.52        9.91
{txt}         06. F. $11,000-$12,999 {c |}{res}         26        2.43       12.34
{txt}         07. G. $13,000-$14,999 {c |}{res}         24        2.24       14.58
{txt}         08. H. $15,000-$16,999 {c |}{res}         28        2.62       17.20
{txt}         09. J. $17,000-$19,999 {c |}{res}         21        1.96       19.16
{txt}         10. K. $20,000-$21,999 {c |}{res}         32        2.99       22.15
{txt}         11. M. $22,000-$24,999 {c |}{res}         47        4.39       26.54
{txt}         12. N. $25,000-$29,999 {c |}{res}         51        4.77       31.31
{txt}         13. P. $30,000-$34,999 {c |}{res}         58        5.42       36.73
{txt}         14. Q. $35,000-$39,999 {c |}{res}         48        4.49       41.21
{txt}         15. R. $40,000-$44,999 {c |}{res}         66        6.17       47.38
{txt}         16. S. $45,000-$49,999 {c |}{res}         49        4.58       51.96
{txt}         17. T. $50,000-$59,999 {c |}{res}         83        7.76       59.72
{txt}         18. U. $60,000-$69,999 {c |}{res}         81        7.57       67.29
{txt}         19. V. $70,000-$79,999 {c |}{res}         77        7.20       74.49
{txt}         20. W. $80,000-$89,999 {c |}{res}         63        5.89       80.37
{txt}        21. X. $90,000-$104,999 {c |}{res}         55        5.14       85.51
{txt}       22. Y. $105,000-$119,000 {c |}{res}         38        3.55       89.07
{txt}       23. Z. $120,000 and over {c |}{res}        117       10.93      100.00
{txt}{hline 32}{c +}{hline 35}
                          Total {c |}{res}      1,070      100.00
{txt}
{com}.         summ income, detail

                    {txt}2004 Household Income
{hline 61}
      Percentiles      Smallest
 1%    {res}        1              1
{txt} 5%    {res}        3              1
{txt}10%    {res}        6              1       {txt}Obs         {res}       1070
{txt}25%    {res}       11              1       {txt}Sum of Wgt. {res}       1070

{txt}50%    {res}       16                      {txt}Mean          {res} 14.94486
                        {txt}Largest       Std. Dev.     {res} 6.000916
{txt}75%    {res}       20             23
{txt}90%    {res}       23             23       {txt}Variance      {res} 36.01099
{txt}95%    {res}       23             23       {txt}Skewness      {res}-.5782062
{txt}99%    {res}       23             23       {txt}Kurtosis      {res} 2.449733
{txt}
{com}.         label var income "Income (2004)"
{txt}
{com}.         
.         summ income

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 6}income {c |}{res}      1070    14.94486    6.000916          1         23
{txt}
{com}.         gen income01 = (income - r(min))/(r(max)-r(min))
{txt}(142 missing values generated)

{com}.         label var income01 "2004 Household Income"
{txt}
{com}. 
.         
. *Borrowing
.         recode V06P545 (1=1) (2=0) (9=.), gen(borrow)
{txt}(102 differences between V06P545 and borrow)

{com}.         label var borrow "Could Borrow Money?"
{txt}
{com}.         label def bor 1 "Could Borrow" 0 "Could Not Borrow"
{txt}
{com}.         label values borrow bor
{txt}
{com}.         tab borrow

    {txt}Could Borrow {c |}
          Money? {c |}      Freq.     Percent        Cum.
{hline 17}{c +}{hline 35}
Could Not Borrow {c |}{res}        101       14.99       14.99
{txt}    Could Borrow {c |}{res}        573       85.01      100.00
{txt}{hline 17}{c +}{hline 35}
           Total {c |}{res}        674      100.00
{txt}
{com}.         
.         gen borrow_total = .
{txt}(1212 missing values generated)

{com}.         replace borrow_total = 0 if borrow == 0
{txt}(101 real changes made)

{com}.         replace borrow_total = V06P547 if borrow_total == . 
{txt}(522 real changes made)

{com}.         label var borrow_total "Total that Could be Borrowed"
{txt}
{com}.         tab borrow_total

 {txt}Total that {c |}
   Could be {c |}
   Borrowed {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        101       16.21       16.21
{txt}          1 {c |}{res}          2        0.32       16.53
{txt}          3 {c |}{res}          1        0.16       16.69
{txt}         10 {c |}{res}          1        0.16       16.85
{txt}         20 {c |}{res}          1        0.16       17.01
{txt}         50 {c |}{res}          2        0.32       17.34
{txt}        100 {c |}{res}         12        1.93       19.26
{txt}        200 {c |}{res}          8        1.28       20.55
{txt}        250 {c |}{res}          1        0.16       20.71
{txt}        300 {c |}{res}          1        0.16       20.87
{txt}        400 {c |}{res}          1        0.16       21.03
{txt}        500 {c |}{res}         24        3.85       24.88
{txt}        600 {c |}{res}          1        0.16       25.04
{txt}        700 {c |}{res}          1        0.16       25.20
{txt}        800 {c |}{res}          1        0.16       25.36
{txt}       1000 {c |}{res}         56        8.99       34.35
{txt}       1500 {c |}{res}          6        0.96       35.31
{txt}       2000 {c |}{res}         28        4.49       39.81
{txt}       2500 {c |}{res}          1        0.16       39.97
{txt}       3000 {c |}{res}         10        1.61       41.57
{txt}       4000 {c |}{res}          4        0.64       42.22
{txt}       5000 {c |}{res}         67       10.75       52.97
{txt}       6000 {c |}{res}          4        0.64       53.61
{txt}       8000 {c |}{res}          1        0.16       53.77
{txt}      10000 {c |}{res}         91       14.61       68.38
{txt}      15000 {c |}{res}          3        0.48       68.86
{txt}      19000 {c |}{res}          1        0.16       69.02
{txt}      20000 {c |}{res}         28        4.49       73.52
{txt}      25000 {c |}{res}          8        1.28       74.80
{txt}      30000 {c |}{res}         15        2.41       77.21
{txt}      40000 {c |}{res}          5        0.80       78.01
{txt}      45000 {c |}{res}          1        0.16       78.17
{txt}      50000 {c |}{res}         45        7.22       85.39
{txt}      60000 {c |}{res}          2        0.32       85.71
{txt}      70000 {c |}{res}          2        0.32       86.04
{txt}      75000 {c |}{res}          1        0.16       86.20
{txt}      80000 {c |}{res}          1        0.16       86.36
{txt}      85000 {c |}{res}          1        0.16       86.52
{txt}     100000 {c |}{res}         52        8.35       94.86
{txt}     125000 {c |}{res}          1        0.16       95.02
{txt}     150000 {c |}{res}          2        0.32       95.35
{txt}     200000 {c |}{res}          6        0.96       96.31
{txt}     250000 {c |}{res}          3        0.48       96.79
{txt}     300000 {c |}{res}          4        0.64       97.43
{txt}     400000 {c |}{res}          1        0.16       97.59
{txt}     500000 {c |}{res}          6        0.96       98.56
{txt}     800000 {c |}{res}          1        0.16       98.72
{txt}    1000000 {c |}{res}          7        1.12       99.84
{txt}    9999999 {c |}{res}          1        0.16      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        623      100.00
{txt}
{com}.         summ borrow_total, detail

                {txt}Total that Could be Borrowed
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        0              0       {txt}Obs         {res}        623
{txt}25%    {res}      600              0       {txt}Sum of Wgt. {res}        623

{txt}50%    {res}     5000                      {txt}Mean          {res} 57316.99
                        {txt}Largest       Std. Dev.     {res} 418276.7
{txt}75%    {res}    30000        1000000
{txt}90%    {res}   100000        1000000       {txt}Variance      {res} 1.75e+11
{txt}95%    {res}   125000        1000000       {txt}Skewness      {res} 21.76214
{txt}99%    {res}  1000000        9999999       {txt}Kurtosis      {res} 514.4438
{txt}
{com}.         
.         
. *Interest
.         foreach var in V06P630 V06P631 V06P632 V06P633 V06P634 {c -(} 
{txt}  2{com}.                         tab `var'
{txt}  3{com}.                         {c )-}

{txt}Mod14_A1. How interested {c |}
  is R in government and {c |}
                politics {c |}      Freq.     Percent        Cum.
{hline 25}{c +}{hline 35}
 1. Extremely interested {c |}{res}         62       18.24       18.24
{txt}      2. Very interested {c |}{res}        134       39.41       57.65
{txt}3. Moderately interested {c |}{res}        113       33.24       90.88
{txt}  4. Slightly interested {c |}{res}         29        8.53       99.41
{txt}5. Not interested at all {c |}{res}          1        0.29       99.71
{txt}              9. Refused {c |}{res}          1        0.29      100.00
{txt}{hline 25}{c +}{hline 35}
                   Total {c |}{res}        340      100.00

{txt}Mod14_A2. How closely {c |}
  R pays attention to {c |}
       government and {c |}
             politics {c |}      Freq.     Percent        Cum.
{hline 22}{c +}{hline 35}
 1. Extremely closely {c |}{res}         37       10.88       10.88
{txt}      2. Very closely {c |}{res}        113       33.24       44.12
{txt}3. Moderately closely {c |}{res}        147       43.24       87.35
{txt}  4. Slightly closely {c |}{res}         36       10.59       97.94
{txt}5. Not closely at all {c |}{res}          7        2.06      100.00
{txt}{hline 22}{c +}{hline 35}
                Total {c |}{res}        340      100.00

   {txt}Mod14_A3. How often {c |}
  does R pay attention {c |}
     to government and {c |}
              politics {c |}      Freq.     Percent        Cum.
{hline 23}{c +}{hline 35}
       1. All the time {c |}{res}         56       16.47       16.47
{txt}   2. Most of the time {c |}{res}        148       43.53       60.00
{txt}3. About half the time {c |}{res}         96       28.24       88.24
{txt}    4. Once in a while {c |}{res}         37       10.88       99.12
{txt}              5. Never {c |}{res}          3        0.88      100.00
{txt}{hline 23}{c +}{hline 35}
                 Total {c |}{res}        340      100.00

          {txt}Mod14_B1. How {c |}
  interested has R been {c |}
       in the political {c |}
              campaigns {c |}      Freq.     Percent        Cum.
{hline 24}{c +}{hline 35}
1. Very much interested {c |}{res}        155       46.27       46.27
{txt} 2. Somewhat interested {c |}{res}        124       37.01       83.28
{txt} 3. Not much interested {c |}{res}         56       16.72      100.00
{txt}{hline 24}{c +}{hline 35}
                  Total {c |}{res}        335      100.00

 {txt}Mod14_B2. How often {c |}
       does R follow {c |}
      government and {c |}
      public affairs {c |}      Freq.     Percent        Cum.
{hline 21}{c +}{hline 35}
 1. Most of the time {c |}{res}        164       48.96       48.96
{txt} 2. Some of the time {c |}{res}        104       31.04       80.00
{txt}3. Only now and then {c |}{res}         46       13.73       93.73
{txt}    4. Hardly at all {c |}{res}         21        6.27      100.00
{txt}{hline 21}{c +}{hline 35}
               Total {c |}{res}        335      100.00
{txt}
{com}.                         
.                 recode V06P630 (1=5) (2=4) (3=3) (4=2) (5=1) (9=.), gen(int_a1)
{txt}(227 differences between V06P630 and int_a1)

{com}.                 recode V06P631  (1=5) (2=4) (3=3) (4=2) (5=1) (9=.), gen(int_a2)
{txt}(193 differences between V06P631 and int_a2)

{com}.                 recode V06P632  (1=5) (2=4) (3=3) (4=2) (5=1) (9=.), gen(int_a3)
{txt}(244 differences between V06P632 and int_a3)

{com}.                 recode V06P633  (1=3) (2=2) (3=1), gen(int_b1)
{txt}(211 differences between V06P633 and int_b1)

{com}.                 recode V06P634  (1=4) (2=3) (3=2) (4=1), gen(int_b2)
{txt}(335 differences between V06P634 and int_b2)

{com}.         
.                 pwcorr int_a1-int_a3, sig

             {txt}{c |}   int_a1   int_a2   int_a3
{hline 13}{c +}{hline 27}
      int_a1 {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
      int_a2 {c |} {res}  0.6563   1.0000 
             {txt}{c |}{res}   0.0000
             {txt}{c |}
      int_a3 {c |} {res}  0.6222   0.6395   1.0000 
             {txt}{c |}{res}   0.0000   0.0000
             {txt}{c |}

{com}.                 pwcorr int_b1 int_b2, sig

             {txt}{c |}   int_b1   int_b2
{hline 13}{c +}{hline 18}
      int_b1 {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
      int_b2 {c |} {res}  0.5821   1.0000 
             {txt}{c |}{res}   0.0000
             {txt}{c |}

{com}.                 
.                 factor int_a1-int_a3, pcf
{txt}(obs=339)

Factor analysis/correlation{col 52}Number of obs    = {res}     339
{col 5}{txt}Method: principal-component factors{col 52}Retained factors = {res}       1
{col 5}{txt}Rotation: (unrotated){col 52}Number of params = {res}       3

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      2.29291      1.91496            0.7643       0.7643
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      0.37795      0.04882            0.1260       0.8903
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}      0.32913            .            0.1097       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}3{txt})  ={res}  422.96{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:int_a1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8678}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2469}}}{space 1}
{space 4}{space 0}{ralign 12:int_a2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8850}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2168}}}{space 1}
{space 4}{space 0}{ralign 12:int_a3}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8699}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2434}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{c  BT}{hline 14}

{com}.                 predict factor1
{txt}(regression scoring assumed)

{p 0 0 2}Scoring coefficients (method = regression){p_end}

{space 4}{hline 13}{c  TT}{hline 10}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}
{space 4}{hline 13}{c   +}{hline 10}
{space 4}{space 0}{ralign 12:int_a1}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.37847}}}{space 1}
{space 4}{space 0}{ralign 12:int_a2}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.38597}}}{space 1}
{space 4}{space 0}{ralign 12:int_a3}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.37937}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}


{com}.                 rename factor1 interest_v1 
{res}{txt}
{com}.                 label var interest_v1 "Interest (Version 1)"
{txt}
{com}.                 factor int_b1 int_b2, pcf
{txt}(obs=335)

Factor analysis/correlation{col 52}Number of obs    = {res}     335
{col 5}{txt}Method: principal-component factors{col 52}Retained factors = {res}       1
{col 5}{txt}Rotation: (unrotated){col 52}Number of params = {res}       1

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      1.58206      1.16411            0.7910       0.7910
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      0.41794            .            0.2090       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}1{txt})  ={res}  137.96{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:int_b1}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8894}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2090}}}{space 1}
{space 4}{space 0}{ralign 12:int_b2}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8894}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2090}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{c  BT}{hline 14}

{com}.                 predict factor1 
{txt}(regression scoring assumed)

{p 0 0 2}Scoring coefficients (method = regression){p_end}

{space 4}{hline 13}{c  TT}{hline 10}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}
{space 4}{hline 13}{c   +}{hline 10}
{space 4}{space 0}{ralign 12:int_b1}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.56218}}}{space 1}
{space 4}{space 0}{ralign 12:int_b2}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.56218}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}


{com}.                 rename factor1 interest_v2
{res}{txt}
{com}.                 label var interest_v2 "Interest (Version 2)"
{txt}
{com}.                 
.                 summ interest_v1 interest_v2

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 1}interest_v1 {c |}{res}       339    4.37e-09           1  -2.449235   1.833793
{txt}{space 1}interest_v2 {c |}{res}       335    1.37e-08           1  -2.362611   1.014366
{txt}
{com}.         
.                 gen interest = interest_v1
{txt}(873 missing values generated)

{com}.                 replace interest = interest_v2 if interest == . 
{txt}(335 real changes made)

{com}.                 summ interest

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 4}interest {c |}{res}       674    9.01e-09    .9992568  -2.449235   1.833793
{txt}
{com}.                 label var interest "Interest (Both Versions)"
{txt}
{com}.                 
.                 
. *Ideology
.                 tab V045117

      {txt}G4a. Liberal/conservative 7-point {c |}
                  scale: self-placement {c |}      Freq.     Percent        Cum.
{hline 40}{c +}{hline 35}
                  01. Extremely liberal {c |}{res}         20        1.88        1.88
{txt}                            02. Liberal {c |}{res}        103        9.66       11.54
{txt}                   03. Slightly liberal {c |}{res}        125       11.73       23.26
{txt}       04. Moderate; middle of the road {c |}{res}        279       26.17       49.44
{txt}              05. Slightly conservative {c |}{res}        143       13.41       62.85
{txt}                       06. Conservative {c |}{res}        166       15.57       78.42
{txt}             07. Extremely conservative {c |}{res}         31        2.91       81.33
{txt}80. Haven't thought much {c -(}DO NOT PROBE{c )-} {c |}{res}        187       17.54       98.87
{txt}                         88. Don't know {c |}{res}         10        0.94       99.81
{txt}                            89. Refused {c |}{res}          2        0.19      100.00
{txt}{hline 40}{c +}{hline 35}
                                  Total {c |}{res}      1,066      100.00
{txt}
{com}.                 gen ideol = V045117
{txt}(146 missing values generated)

{com}.                 replace ideol = . if ideol > 8
{txt}(199 real changes made, 199 to missing)

{com}.                 label def id 1 "Ext. Liberal" 2 "Liberal" 3 "Slightly Liberal" 4 "Moderate" 5 "Slight Cons." 6 "Conservative" 7 "Ext. Conservative"
{txt}
{com}.                 label values ideol id
{txt}
{com}.                 tab ideol

            {txt}ideol {c |}      Freq.     Percent        Cum.
{hline 18}{c +}{hline 35}
     Ext. Liberal {c |}{res}         20        2.31        2.31
{txt}          Liberal {c |}{res}        103       11.88       14.19
{txt} Slightly Liberal {c |}{res}        125       14.42       28.60
{txt}         Moderate {c |}{res}        279       32.18       60.78
{txt}     Slight Cons. {c |}{res}        143       16.49       77.28
{txt}     Conservative {c |}{res}        166       19.15       96.42
{txt}Ext. Conservative {c |}{res}         31        3.58      100.00
{txt}{hline 18}{c +}{hline 35}
            Total {c |}{res}        867      100.00
{txt}
{com}.                 tab V045117 ideol

                 {txt}G4a. {c |}
 Liberal/conservative {c |}
       7-point scale: {c |}                               ideol
       self-placement {c |} Ext. Libe    Liberal  Slightly    Moderate  Slight Co  Conservat {c |}     Total
{hline 22}{c +}{hline 66}{c +}{hline 10}
01. Extremely liberal {c |}{res}        20          0          0          0          0          0 {txt}{c |}{res}        20 
{txt}          02. Liberal {c |}{res}         0        103          0          0          0          0 {txt}{c |}{res}       103 
{txt} 03. Slightly liberal {c |}{res}         0          0        125          0          0          0 {txt}{c |}{res}       125 
{txt}04. Moderate; middle  {c |}{res}         0          0          0        279          0          0 {txt}{c |}{res}       279 
{txt}05. Slightly conserva {c |}{res}         0          0          0          0        143          0 {txt}{c |}{res}       143 
{txt}     06. Conservative {c |}{res}         0          0          0          0          0        166 {txt}{c |}{res}       166 
{txt}07. Extremely conserv {c |}{res}         0          0          0          0          0          0 {txt}{c |}{res}        31 
{txt}{hline 22}{c +}{hline 66}{c +}{hline 10}
                Total {c |}{res}        20        103        125        279        143        166 {txt}{c |}{res}       867 


                 {txt}G4a. {c |}
 Liberal/conservative {c |}
       7-point scale: {c |}   ideol
       self-placement {c |} Ext. Cons {c |}     Total
{hline 22}{c +}{hline 11}{c +}{hline 10}
01. Extremely liberal {c |}{res}         0 {txt}{c |}{res}        20 
{txt}          02. Liberal {c |}{res}         0 {txt}{c |}{res}       103 
{txt} 03. Slightly liberal {c |}{res}         0 {txt}{c |}{res}       125 
{txt}04. Moderate; middle  {c |}{res}         0 {txt}{c |}{res}       279 
{txt}05. Slightly conserva {c |}{res}         0 {txt}{c |}{res}       143 
{txt}     06. Conservative {c |}{res}         0 {txt}{c |}{res}       166 
{txt}07. Extremely conserv {c |}{res}        31 {txt}{c |}{res}        31 
{txt}{hline 22}{c +}{hline 11}{c +}{hline 10}
                Total {c |}{res}        31 {txt}{c |}{res}       867 

{txt}
{com}.                 
.                 recode ideol (1=4) (2=3) (3=2) (4=1) (5=2) (6=3) (7=4), gen(ideol_str)
{txt}(867 differences between ideol and ideol_str)

{com}.                 label var ideol_str "Ideological Extremity"
{txt}
{com}.                 
.                 summ ideol_str

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 3}ideol_str {c |}{res}       867    2.106113    .9257901          1          4
{txt}
{com}.                 gen ideol_str01 = (ideol_str - r(min))/(r(max)-r(min))
{txt}(345 missing values generated)

{com}.                 label var ideol_str01 "Ideological Extremity"
{txt}
{com}.                 
. *2004 Retro/Prosp Evaluations*
.         label def bette1 1 "Much Worse" 2 "Somewhat Worse" 3 "Same" 4 "Somewhat Better" 5 "Much Better"
{txt}
{com}.         recode  V043098 (1=5) (2=4) (3=3) (4=2) (5=1) , gen(retro2004)
{txt}(820 differences between V043098 and retro2004)

{com}.         label var retro2004 "Retro Econ Evals (2004)"
{txt}
{com}.         label values retro2004 bette1
{txt}
{com}.         tab retro2004

     {txt}Retro Econ {c |}
   Evals (2004) {c |}      Freq.     Percent        Cum.
{hline 16}{c +}{hline 35}
     Much Worse {c |}{res}        234       19.53       19.53
{txt} Somewhat Worse {c |}{res}        308       25.71       45.24
{txt}           Same {c |}{res}        378       31.55       76.79
{txt}Somewhat Better {c |}{res}        236       19.70       96.49
{txt}    Much Better {c |}{res}         42        3.51      100.00
{txt}{hline 16}{c +}{hline 35}
          Total {c |}{res}      1,198      100.00
{txt}
{com}.         summ retro2004

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 3}retro2004 {c |}{res}      1198    2.619366    1.109852          1          5
{txt}
{com}.         
.         recode V043100 (1=5) (2=4) (3=3) (4=2) (5=1)  (8=.), gen(prosp2004)
{txt}(621 differences between V043100 and prosp2004)

{com}.         label var prosp2004 "Prosp Econ Evals (2004)
{txt}
{com}.         label values prosp2004 bette
{txt}
{com}.         tab prosp2004

 {txt}Prosp Econ {c |}
      Evals {c |}
     (2004) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}         48        4.15        4.15
{txt}          2 {c |}{res}        160       13.83       17.98
{txt}          3 {c |}{res}        537       46.41       64.39
{txt}          4 {c |}{res}        316       27.31       91.70
{txt}          5 {c |}{res}         96        8.30      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,157      100.00
{txt}
{com}.         summ prosp2004

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 3}prosp2004 {c |}{res}      1157    3.217805    .9287382          1          5
{txt}
{com}.         
.         recode V043102 (1=5) (2=4) (3=3) (4=2) (5=1) (8=.), gen(retro_unempl2004)
{txt}(767 differences between V043102 and retro_unempl2004)

{com}.         label var retro_unempl2004 "Retro Unempl. Evals (2004)"
{txt}
{com}.         label values retro_unempl2004 bette1
{txt}
{com}.         tab retro_unempl2004

  {txt}Retro Unempl. {c |}
   Evals (2004) {c |}      Freq.     Percent        Cum.
{hline 16}{c +}{hline 35}
     Much Worse {c |}{res}        208       19.53       19.53
{txt} Somewhat Worse {c |}{res}        292       27.42       46.95
{txt}           Same {c |}{res}        299       28.08       75.02
{txt}Somewhat Better {c |}{res}        232       21.78       96.81
{txt}    Much Better {c |}{res}         34        3.19      100.00
{txt}{hline 16}{c +}{hline 35}
          Total {c |}{res}      1,065      100.00
{txt}
{com}.         summ retro_unempl2004

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
retro_u~2004 {c |}{res}      1065    2.616901    1.120425          1          5
{txt}
{com}.         
.         recode V043103 (1=1) (3=2) (5=3) (0=.) (8=.) (9=.), gen(prosp_unempl2004)
{txt}(958 differences between V043103 and prosp_unempl2004)

{com}.         label var prosp_unempl2004 "Prosp Unempl. Evals (2004)"
{txt}
{com}.         label def prun 1 "More Unmployment" 2 "Same" 3 "Less Unemployment"
{txt}
{com}.         label values prosp_unempl2004 prun
{txt}
{com}.         tab prosp_unempl2004

    {txt}Prosp Unempl. {c |}
     Evals (2004) {c |}      Freq.     Percent        Cum.
{hline 18}{c +}{hline 35}
 More Unmployment {c |}{res}        254       24.35       24.35
{txt}             Same {c |}{res}        557       53.40       77.76
{txt}Less Unemployment {c |}{res}        232       22.24      100.00
{txt}{hline 18}{c +}{hline 35}
            Total {c |}{res}      1,043      100.00
{txt}
{com}.         summ prosp_unempl2004

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
prosp_u~2004 {c |}{res}      1043    1.978907    .6826166          1          3
{txt}
{com}.         
.         recode  V043105 (1=5) (2=4) (3=3) (4=2) (5=1), gen(retro_inflation2004)
{txt}(579 differences between V043105 and retro_inflation2004)

{com}.         label var retro_inflation2004 "Retro Inflation (2004)"
{txt}
{com}.         label values retro_inflation2004 bette1
{txt}
{com}.         summ retro_inflation2004

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
retro_i~2004 {c |}{res}      1048    2.400763    .8267723          1          5
{txt}
{com}.         tab retro_inflation2004

{txt}Retro Inflation {c |}
         (2004) {c |}      Freq.     Percent        Cum.
{hline 16}{c +}{hline 35}
     Much Worse {c |}{res}        165       15.74       15.74
{txt} Somewhat Worse {c |}{res}        359       34.26       50.00
{txt}           Same {c |}{res}        469       44.75       94.75
{txt}Somewhat Better {c |}{res}         49        4.68       99.43
{txt}    Much Better {c |}{res}          6        0.57      100.00
{txt}{hline 16}{c +}{hline 35}
          Total {c |}{res}      1,048      100.00
{txt}
{com}.         
.         recode  V043106 (1=1) (3=2) (5=3) (0=.) (8=.) (9=.), gen(prosp_inflation2004)
{txt}(851 differences between V043106 and prosp_inflation2004)

{com}.         label var prosp_inflation2004 "Prosp. Inflation (2004)"
{txt}
{com}.         label def prun1 1 "More Inflation" 2 "Same" 3 "Lower Inflation"
{txt}
{com}.         label values prosp_inflation2004 prun1
{txt}
{com}.         tab prosp_inflation2004

         {txt}Prosp. {c |}
      Inflation {c |}
         (2004) {c |}      Freq.     Percent        Cum.
{hline 16}{c +}{hline 35}
 More Inflation {c |}{res}        361       35.36       35.36
{txt}           Same {c |}{res}        588       57.59       92.95
{txt}Lower Inflation {c |}{res}         72        7.05      100.00
{txt}{hline 16}{c +}{hline 35}
          Total {c |}{res}      1,021      100.00
{txt}
{com}.         summ prosp_inflation2004

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
prosp_i~2004 {c |}{res}      1021    1.716944    .5867799          1          3
{txt}
{com}.                 
.         pwcorr retro2004 prosp2004 retro_unempl2004 prosp_unempl2004 retro_inflation2004 prosp_inflation2004, sig

             {txt}{c |} retro2~4 prosp2~4 re~l2004 pr~l2004 re~n2004 pr~n2004
{hline 13}{c +}{hline 54}
   retro2004 {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
   prosp2004 {c |} {res}  0.3718   1.0000 
             {txt}{c |}{res}   0.0000
             {txt}{c |}
retro_u~2004 {c |} {res}  0.6636   0.3060   1.0000 
             {txt}{c |}{res}   0.0000   0.0000
             {txt}{c |}
prosp_u~2004 {c |} {res}  0.3658   0.4566   0.3583   1.0000 
             {txt}{c |}{res}   0.0000   0.0000   0.0000
             {txt}{c |}
retro_i~2004 {c |} {res}  0.4453   0.2697   0.3904   0.2396   1.0000 
             {txt}{c |}{res}   0.0000   0.0000   0.0000   0.0000
             {txt}{c |}
prosp_i~2004 {c |} {res}  0.1204   0.3222   0.1068   0.2406   0.1309   1.0000 
             {txt}{c |}{res}   0.0001   0.0000   0.0007   0.0000   0.0000
             {txt}{c |}

{com}.         factor retro2004 prosp2004 retro_unempl2004 prosp_unempl2004 retro_inflation2004 prosp_inflation2004, pcf
{txt}(obs=954)

Factor analysis/correlation{col 52}Number of obs    = {res}     954
{col 5}{txt}Method: principal-component factors{col 52}Retained factors = {res}       2
{col 5}{txt}Rotation: (unrotated){col 52}Number of params = {res}      11

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      2.69064      1.61144            0.4484       0.4484
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      1.07920      0.33606            0.1799       0.6283
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}      0.74314      0.11467            0.1239       0.7522
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}      0.62847      0.10302            0.1047       0.8569
{txt}{col 5}{ralign 11:Factor5}  {c |}{res}      0.52545      0.19233            0.0876       0.9445
{txt}{col 5}{ralign 11:Factor6}  {c |}{res}      0.33311            .            0.0555       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}15{txt}) ={res} 1367.91{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:retro2004}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7975}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.3373}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2503}}}{space 1}
{space 4}{space 0}{ralign 12:prosp2004}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6900}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.3749}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3834}}}{space 1}
{space 4}{space 0}{ralign 12:retro_u~2004}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7580}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.3700}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2885}}}{space 1}
{space 4}{space 0}{ralign 12:prosp_u~2004}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6818}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.2514}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4720}}}{space 1}
{space 4}{space 0}{ralign 12:retro_i~2004}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6194}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2784}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5389}}}{space 1}
{space 4}{space 0}{ralign 12:prosp_i~2004}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3945}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.7398}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2971}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{c  BT}{hline 14}

{com}.         rotate

{txt}Factor analysis/correlation{col 52}Number of obs    = {res}     954
{col 5}{txt}Method: principal-component factors{col 52}Retained factors = {res}       2
{col 5}{txt}Rotation: orthogonal varimax (Kaiser off){col 52}Number of params = {res}      11

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Variance}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      2.23045      0.69107            0.3717       0.3717
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      1.53938            .            0.2566       0.6283
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}15{txt}) ={res} 1367.91{txt} Prob>chi2 ={res} 0.0000

{txt}Rotated factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:retro2004}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8543}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1410}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2503}}}{space 1}
{space 4}{space 0}{ralign 12:prosp2004}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.3829}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.6856}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3834}}}{space 1}
{space 4}{space 0}{ralign 12:retro_u~2004}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8384}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0923}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2885}}}{space 1}
{space 4}{space 0}{ralign 12:prosp_u~2004}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4419}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.5768}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4720}}}{space 1}
{space 4}{space 0}{ralign 12:retro_i~2004}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6722}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0957}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.5389}}}{space 1}
{space 4}{space 0}{ralign 12:prosp_i~2004}{space 1}{c |}{space 1}{ralign 8:{res:{sf: -0.0619}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.8361}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2971}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{c  BT}{hline 14}

Factor rotation matrix

{space 4}{hline 13}{c  TT}{hline 9}{hline 9}
{space 4}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 7:Factor1}{space 1}{space 1}{ralign 7:Factor2}{space 1}
{space 4}{hline 13}{c   +}{hline 9}{hline 9}
{space 4}{space 0}{ralign 12:Factor1}{space 1}{c |}{space 1}{ralign 7:{res:{sf: 0.8452}}}{space 1}{space 1}{ralign 7:{res:{sf: 0.5344}}}{space 1}
{space 4}{space 0}{ralign 12:Factor2}{space 1}{c |}{space 1}{ralign 7:{res:{sf:-0.5344}}}{space 1}{space 1}{ralign 7:{res:{sf: 0.8452}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 9}{hline 9}

{com}.         predict factor1 factor2
{txt}(regression scoring assumed)

{p 0 0 2}Scoring coefficients (method = regression; based on varimax rotated factors){p_end}

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}
{space 4}{space 0}{ralign 12:retro2004}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.41754}}}{space 1}{space 1}{ralign 8:{res:{sf:-0.10580}}}{space 1}
{space 4}{space 0}{ralign 12:prosp2004}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.03114}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.43063}}}{space 1}
{space 4}{space 0}{ralign 12:retro_u~2004}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.42135}}}{space 1}{space 1}{ralign 8:{res:{sf:-0.13927}}}{space 1}
{space 4}{space 0}{ralign 12:prosp_u~2004}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.08970}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.33229}}}{space 1}
{space 4}{space 0}{ralign 12:retro_i~2004}{space 1}{c |}{space 1}{ralign 8:{res:{sf: 0.33240}}}{space 1}{space 1}{ralign 8:{res:{sf:-0.09500}}}{space 1}
{space 4}{space 0}{ralign 12:prosp_i~2004}{space 1}{c |}{space 1}{ralign 8:{res:{sf:-0.24240}}}{space 1}{space 1}{ralign 8:{res:{sf: 0.65777}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}


{com}.         rename factor1 retro2004_factor
{res}{txt}
{com}.         label var retro2004_factor "Retro Evals (2004; Factor)"
{txt}
{com}.         rename factor2 prosp2004_factor 
{res}{txt}
{com}.         label var prosp2004_factor "Prosp Evals (2004; Factor)"
{txt}
{com}. 
. 
.         
. *Network Interest
.                 foreach var in V06P618 V06P619 V06P620 {c -(}
{txt}  2{com}.                         tab `var'
{txt}  3{com}.                         {c )-}

         {txt}Mod13_12_1. How {c |}
      interested Network {c |}
   person #1 in govt and {c |}
                politics {c |}      Freq.     Percent        Cum.
{hline 25}{c +}{hline 35}
 1. Extremely interested {c |}{res}        129       27.10       27.10
{txt}      2. Very interested {c |}{res}        154       32.35       59.45
{txt}3. Moderately interested {c |}{res}        130       27.31       86.76
{txt}  4. Slightly interested {c |}{res}         52       10.92       97.69
{txt}5. Not interested at all {c |}{res}         10        2.10       99.79
{txt}           8. Don't know {c |}{res}          1        0.21      100.00
{txt}{hline 25}{c +}{hline 35}
                   Total {c |}{res}        476      100.00

         {txt}Mod13_12_2. How {c |}
      interested Network {c |}
   person #2 in govt and {c |}
                politics {c |}      Freq.     Percent        Cum.
{hline 25}{c +}{hline 35}
 1. Extremely interested {c |}{res}         84       19.67       19.67
{txt}      2. Very interested {c |}{res}        131       30.68       50.35
{txt}3. Moderately interested {c |}{res}        154       36.07       86.42
{txt}  4. Slightly interested {c |}{res}         46       10.77       97.19
{txt}5. Not interested at all {c |}{res}         11        2.58       99.77
{txt}           8. Don't know {c |}{res}          1        0.23      100.00
{txt}{hline 25}{c +}{hline 35}
                   Total {c |}{res}        427      100.00

         {txt}Mod13_12_3. How {c |}
      interested Network {c |}
   person #3 in govt and {c |}
                politics {c |}      Freq.     Percent        Cum.
{hline 25}{c +}{hline 35}
 1. Extremely interested {c |}{res}         57       16.62       16.62
{txt}      2. Very interested {c |}{res}        106       30.90       47.52
{txt}3. Moderately interested {c |}{res}        123       35.86       83.38
{txt}  4. Slightly interested {c |}{res}         46       13.41       96.79
{txt}5. Not interested at all {c |}{res}          9        2.62       99.42
{txt}           8. Don't know {c |}{res}          2        0.58      100.00
{txt}{hline 25}{c +}{hline 35}
                   Total {c |}{res}        343      100.00
{txt}
{com}.                 
.                 recode V06P618 (1=5) (2=4) (3=3) (4=2) (5=1) (8=.), gen(disc1_interest)
{txt}(346 differences between V06P618 and disc1_interest)

{com}.                 recode V06P619 (1=5) (2=4) (3=3) (4=2) (5=1) (8=.), gen(disc2_interest)
{txt}(273 differences between V06P619 and disc2_interest)

{com}.                 recode V06P620 (1=5) (2=4) (3=3) (4=2) (5=1) (8=.), gen(disc3_interest)
{txt}(220 differences between V06P620 and disc3_interest)

{com}.         
.                 egen disc_interest = rowmean(disc1_interest disc2_interest disc3_interest)
{txt}(736 missing values generated)

{com}.                 label var disc_interest "Network Pol. Interest"
{txt}
{com}.                 
.                 summ disc_interest

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
disc_inter~t {c |}{res}       476    3.596289    .7590439          1          5
{txt}
{com}.                 gen disc_int01 = (disc_interest - r(min))/(r(max)-r(min))
{txt}(736 missing values generated)

{com}.                 label var disc_int01 "Network Interest"
{txt}
{com}.                 
.                 
.                 *Weighted Scale of Exposure to Disagreement
.                 
.                         *Agree/Weight
.                         gen a1i = d1_agree * disc1_interest
{txt}(747 missing values generated)

{com}.                         gen a2i = d2_agree * disc2_interest
{txt}(792 missing values generated)

{com}.                         gen a3i = d3_agree * disc3_interest
{txt}(876 missing values generated)

{com}.                         egen agree_int = rowtotal(a1i a2i a3i), missing
{txt}(742 missing values generated)

{com}.                 
.                         *Disagree/Weight
.                         gen d1i = d1_disagree * disc1_interest
{txt}(747 missing values generated)

{com}.                         gen d2i = d2_disagree * disc2_interest
{txt}(792 missing values generated)

{com}.                         gen d3i = d3_disagree * disc3_interest
{txt}(876 missing values generated)

{com}.                         egen disagree_int = rowtotal(d1i d2i d3i), missing
{txt}(742 missing values generated)

{com}.                 
.                         *Scale
.                                 gen disagree_total_int = disagree_int - agree_int
{txt}(742 missing values generated)

{com}.                                 gen disagree_avg_int = disagree_total_int/(pid_disagree + pid_agree)
{txt}(742 missing values generated)

{com}.                                 label var disagree_total_int "Network Disagreement (Interest Weighted)"
{txt}
{com}.                                 label var disagree_avg_int "Network Disagreement (Interest Weighted)"
{txt}
{com}.                                 
.                                 foreach var in disagree_total_int disagree_avg_int {c -(}
{txt}  2{com}.                                         summ `var'
{txt}  3{com}.                                         gen `var'01 = (`var' - r(min))/(r(max)-r(min))
{txt}  4{com}.                                         {c )-}

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
disagr~l_int {c |}{res}       470   -4.355319    6.174724        -15         14
{txt}(742 missing values generated)

    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
disagr~g_int {c |}{res}       470   -1.658511    2.431018         -5          5
{txt}(742 missing values generated)

{com}.                                 
.                                 label var disagree_total_int01 "Network Disagreement (Interest Weighted)"
{txt}
{com}.                                 label var disagree_avg_int01 "Network Disagreement (Interest Weighted)"
{txt}
{com}.                                 
.                                 
.                 
. *Frequency of Talking
.                 foreach var in V06P591x V06P593x V06P595x {c -(} 
{txt}  2{com}.                         tab `var' 
{txt}  3{com}.                         summ `var'
{txt}  4{com}.                         {c )-}

     {txt}Mod13_4_1x. {c |}
  SUMMARY: Total {c |}
     frequency R {c |}
     talked with {c |}
Network person#1 {c |}      Freq.     Percent        Cum.
{hline 17}{c +}{hline 35}
               0 {c |}{res}          2        0.42        0.42
{txt}               1 {c |}{res}          6        1.26        1.68
{txt}               2 {c |}{res}          8        1.68        3.36
{txt}               3 {c |}{res}          9        1.89        5.25
{txt}               4 {c |}{res}          6        1.26        6.51
{txt}               5 {c |}{res}         13        2.73        9.24
{txt}               6 {c |}{res}         10        2.10       11.34
{txt}               7 {c |}{res}          1        0.21       11.55
{txt}               8 {c |}{res}          2        0.42       11.97
{txt}               9 {c |}{res}          1        0.21       12.18
{txt}              10 {c |}{res}         15        3.15       15.34
{txt}              11 {c |}{res}          1        0.21       15.55
{txt}              12 {c |}{res}         18        3.78       19.33
{txt}              15 {c |}{res}          6        1.26       20.59
{txt}              18 {c |}{res}          2        0.42       21.01
{txt}              20 {c |}{res}         17        3.57       24.58
{txt}              24 {c |}{res}          8        1.68       26.26
{txt}              25 {c |}{res}          6        1.26       27.52
{txt}              26 {c |}{res}         18        3.78       31.30
{txt}              28 {c |}{res}          1        0.21       31.51
{txt}              30 {c |}{res}         14        2.94       34.45
{txt}              35 {c |}{res}          3        0.63       35.08
{txt}              40 {c |}{res}          2        0.42       35.50
{txt}              45 {c |}{res}          1        0.21       35.71
{txt}              48 {c |}{res}          2        0.42       36.13
{txt}              50 {c |}{res}          3        0.63       36.76
{txt}              52 {c |}{res}         19        3.99       40.76
{txt}              55 {c |}{res}          1        0.21       40.97
{txt}              60 {c |}{res}          7        1.47       42.44
{txt}              70 {c |}{res}          1        0.21       42.65
{txt}              72 {c |}{res}          1        0.21       42.86
{txt}              75 {c |}{res}          2        0.42       43.28
{txt}              78 {c |}{res}         14        2.94       46.22
{txt}              80 {c |}{res}          1        0.21       46.43
{txt}              90 {c |}{res}          8        1.68       48.11
{txt}             100 {c |}{res}         14        2.94       51.05
{txt}             104 {c |}{res}         12        2.52       53.57
{txt}             108 {c |}{res}          1        0.21       53.78
{txt}             110 {c |}{res}          1        0.21       53.99
{txt}             120 {c |}{res}          8        1.68       55.67
{txt}             125 {c |}{res}          1        0.21       55.88
{txt}             130 {c |}{res}         15        3.15       59.03
{txt}             150 {c |}{res}          5        1.05       60.08
{txt}             156 {c |}{res}          6        1.26       61.34
{txt}             160 {c |}{res}          3        0.63       61.97
{txt}             165 {c |}{res}          2        0.42       62.39
{txt}             170 {c |}{res}          1        0.21       62.61
{txt}             174 {c |}{res}          1        0.21       62.82
{txt}             175 {c |}{res}          2        0.42       63.24
{txt}             180 {c |}{res}         20        4.20       67.44
{txt}             182 {c |}{res}          2        0.42       67.86
{txt}             183 {c |}{res}        153       32.14      100.00
{txt}{hline 17}{c +}{hline 35}
           Total {c |}{res}        476      100.00

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 4}V06P591x {c |}{res}       476    99.80672    74.13953          0        183

     {txt}Mod13_4_2x. {c |}
  SUMMARY: Total {c |}
     frequency R {c |}
     talked with {c |}
Network person#2 {c |}      Freq.     Percent        Cum.
{hline 17}{c +}{hline 35}
               0 {c |}{res}          2        0.47        0.47
{txt}               1 {c |}{res}          2        0.47        0.94
{txt}               2 {c |}{res}          5        1.17        2.11
{txt}               3 {c |}{res}          9        2.11        4.22
{txt}               4 {c |}{res}          9        2.11        6.32
{txt}               5 {c |}{res}         11        2.58        8.90
{txt}               6 {c |}{res}         15        3.51       12.41
{txt}               7 {c |}{res}          1        0.23       12.65
{txt}               8 {c |}{res}          4        0.94       13.58
{txt}               9 {c |}{res}          1        0.23       13.82
{txt}              10 {c |}{res}         12        2.81       16.63
{txt}              12 {c |}{res}         13        3.04       19.67
{txt}              13 {c |}{res}          3        0.70       20.37
{txt}              15 {c |}{res}         10        2.34       22.72
{txt}              16 {c |}{res}          1        0.23       22.95
{txt}              17 {c |}{res}          1        0.23       23.19
{txt}              18 {c |}{res}          1        0.23       23.42
{txt}              20 {c |}{res}         14        3.28       26.70
{txt}              24 {c |}{res}          8        1.87       28.57
{txt}              25 {c |}{res}         11        2.58       31.15
{txt}              26 {c |}{res}         26        6.09       37.24
{txt}              28 {c |}{res}          1        0.23       37.47
{txt}              30 {c |}{res}         12        2.81       40.28
{txt}              35 {c |}{res}          2        0.47       40.75
{txt}              40 {c |}{res}          6        1.41       42.15
{txt}              45 {c |}{res}          3        0.70       42.86
{txt}              48 {c |}{res}          4        0.94       43.79
{txt}              50 {c |}{res}          8        1.87       45.67
{txt}              52 {c |}{res}         14        3.28       48.95
{txt}              55 {c |}{res}          1        0.23       49.18
{txt}              60 {c |}{res}          8        1.87       51.05
{txt}              70 {c |}{res}          5        1.17       52.22
{txt}              72 {c |}{res}          2        0.47       52.69
{txt}              75 {c |}{res}          1        0.23       52.93
{txt}              78 {c |}{res}         22        5.15       58.08
{txt}              80 {c |}{res}          3        0.70       58.78
{txt}              90 {c |}{res}          7        1.64       60.42
{txt}             100 {c |}{res}         16        3.75       64.17
{txt}             104 {c |}{res}          6        1.41       65.57
{txt}             120 {c |}{res}          9        2.11       67.68
{txt}             125 {c |}{res}          2        0.47       68.15
{txt}             130 {c |}{res}         15        3.51       71.66
{txt}             140 {c |}{res}          1        0.23       71.90
{txt}             150 {c |}{res}          7        1.64       73.54
{txt}             156 {c |}{res}          1        0.23       73.77
{txt}             160 {c |}{res}          3        0.70       74.47
{txt}             170 {c |}{res}          1        0.23       74.71
{txt}             175 {c |}{res}          1        0.23       74.94
{txt}             180 {c |}{res}         16        3.75       78.69
{txt}             182 {c |}{res}          3        0.70       79.39
{txt}             183 {c |}{res}         85       19.91       99.30
{txt}8888. Don't know {c |}{res}          3        0.70      100.00
{txt}{hline 17}{c +}{hline 35}
           Total {c |}{res}        427      100.00

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 4}V06P593x {c |}{res}       427    143.7237    739.6108          0       8888

     {txt}Mod13_4_3x. {c |}
  SUMMARY: Total {c |}
     frequency R {c |}
     talked with {c |}
Network person#3 {c |}      Freq.     Percent        Cum.
{hline 17}{c +}{hline 35}
               1 {c |}{res}          2        0.58        0.58
{txt}               2 {c |}{res}          6        1.75        2.33
{txt}               3 {c |}{res}          4        1.17        3.50
{txt}               4 {c |}{res}         10        2.92        6.41
{txt}               5 {c |}{res}         12        3.50        9.91
{txt}               6 {c |}{res}         10        2.92       12.83
{txt}               7 {c |}{res}          1        0.29       13.12
{txt}               8 {c |}{res}         11        3.21       16.33
{txt}              10 {c |}{res}         14        4.08       20.41
{txt}              12 {c |}{res}         11        3.21       23.62
{txt}              14 {c |}{res}          1        0.29       23.91
{txt}              15 {c |}{res}          9        2.62       26.53
{txt}              18 {c |}{res}          1        0.29       26.82
{txt}              20 {c |}{res}         18        5.25       32.07
{txt}              24 {c |}{res}          6        1.75       33.82
{txt}              25 {c |}{res}         10        2.92       36.73
{txt}              26 {c |}{res}         11        3.21       39.94
{txt}              30 {c |}{res}         16        4.66       44.61
{txt}              35 {c |}{res}          2        0.58       45.19
{txt}              36 {c |}{res}          1        0.29       45.48
{txt}              40 {c |}{res}          5        1.46       46.94
{txt}              45 {c |}{res}          1        0.29       47.23
{txt}              48 {c |}{res}          3        0.87       48.10
{txt}              50 {c |}{res}         10        2.92       51.02
{txt}              52 {c |}{res}         20        5.83       56.85
{txt}              60 {c |}{res}          5        1.46       58.31
{txt}              65 {c |}{res}          1        0.29       58.60
{txt}              70 {c |}{res}          2        0.58       59.18
{txt}              72 {c |}{res}          2        0.58       59.77
{txt}              78 {c |}{res}         14        4.08       63.85
{txt}              80 {c |}{res}          2        0.58       64.43
{txt}              90 {c |}{res}          6        1.75       66.18
{txt}              96 {c |}{res}          1        0.29       66.47
{txt}             100 {c |}{res}         15        4.37       70.85
{txt}             104 {c |}{res}          8        2.33       73.18
{txt}             105 {c |}{res}          1        0.29       73.47
{txt}             110 {c |}{res}          1        0.29       73.76
{txt}             120 {c |}{res}          5        1.46       75.22
{txt}             130 {c |}{res}         10        2.92       78.13
{txt}             140 {c |}{res}          2        0.58       78.72
{txt}             150 {c |}{res}          2        0.58       79.30
{txt}             156 {c |}{res}          3        0.87       80.17
{txt}             160 {c |}{res}          2        0.58       80.76
{txt}             170 {c |}{res}          2        0.58       81.34
{txt}             175 {c |}{res}          1        0.29       81.63
{txt}             180 {c |}{res}          7        2.04       83.67
{txt}             182 {c |}{res}          1        0.29       83.97
{txt}             183 {c |}{res}         55       16.03      100.00
{txt}{hline 17}{c +}{hline 35}
           Total {c |}{res}        343      100.00

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 4}V06P595x {c |}{res}       343    71.86297    65.60132          1        183
{txt}
{com}.                 
.                 mvdecode V06P593x, mv(8888 = .a)
    {txt}V06P593x:{res}{col 15}3{txt} missing values generated

{com}.                 
.                 egen disc_freq = rowmean(V06P591x V06P593x V06P595x)
{txt}(736 missing values generated)

{com}.                 label var disc_freq "Avg. #Days in Past 6mos Talked with Discussants"
{txt}
{com}.         
.                 *WeightedScale
.                         foreach var in V06P591x V06P593x V06P595x {c -(}
{txt}  2{com}.                                 egen cut_`var' = cut(`var'), group(5)
{txt}  3{com}.                                 replace cut_`var' = cut_`var' + 1
{txt}  4{com}.                                 {c )-}
{txt}(736 missing values generated)
(476 real changes made)
(788 missing values generated)
(424 real changes made)
(869 missing values generated)
(343 real changes made)

{com}. 
.                 
.                         
.                         *Agree
.                                 gen a1f = d1_agree * cut_V06P591x
{txt}(746 missing values generated)

{com}.                                 gen a2f = d2_agree * cut_V06P593x
{txt}(794 missing values generated)

{com}.                                 gen a3f = d3_agree * cut_V06P595x
{txt}(875 missing values generated)

{com}.                                 egen agree_freq = rowtotal(a1f a2f a3f), missing
{txt}(742 missing values generated)

{com}.                         *Disagree
.                                 gen d1f = d1_disagree * cut_V06P591x
{txt}(746 missing values generated)

{com}.                                 gen d2f = d2_disagree * cut_V06P593x
{txt}(794 missing values generated)

{com}.                                 gen d3f = d3_disagree * cut_V06P595x
{txt}(875 missing values generated)

{com}.                                 egen disagree_freq = rowtotal(d1f d2f d3f), missing
{txt}(742 missing values generated)

{com}.                                 
.                         *Scale
.                                 gen disagree_total_freq = disagree_freq - agree_freq
{txt}(742 missing values generated)

{com}.                                 gen disagree_avg_freq = disagree_total_freq/(pid_disagree + pid_agree)
{txt}(742 missing values generated)

{com}.                                 label var disagree_total_freq "Network Disagreement (Frequency Weighted)"
{txt}
{com}.                                 label var disagree_avg_freq "Network Disagreement (Frequency Weighted)"
{txt}
{com}.                                 
.                                 foreach var in disagree_total_freq disagree_avg_freq {c -(}
{txt}  2{com}.                                         summ `var'
{txt}  3{com}.                                         gen `var'01 = (`var' - r(min))/(r(max)-r(min))
{txt}  4{com}.                                         {c )-}

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
disagree_t~q {c |}{res}       470   -3.893617     5.67467        -15         15
{txt}(742 missing values generated)

    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
disagree_a~q {c |}{res}       470   -1.514894    2.223214         -5          5
{txt}(742 missing values generated)

{com}.                                 
.                                 label var disagree_total_freq01 "Network Disagreement (Frequency Weighted)"
{txt}
{com}.                                 label var disagree_avg_freq01 "Network Disagreement (Frequency Weighted)"
{txt}
{com}. 
. *Tie Closeness*
.         mvdecode V06P590 V06P592 V06P594, mv(8=.)
     {txt}V06P592:{res}{col 15}2{txt} missing values generated

{com}.         
.         recode V06P590 V06P592 V06P594 (1=5) (2=4) (3=3) (4=2) (5=1), gen(close1 close2 close3)
{txt}(408 differences between V06P590 and close1)
(346 differences between V06P592 and close2)
(264 differences between V06P594 and close3)

{com}.         
.         *Agree
.                 gen a1c = d1_agree * close1
{txt}(746 missing values generated)

{com}.                 gen a2c = d2_agree * close2
{txt}(793 missing values generated)

{com}.                 gen a3c = d3_agree * close3
{txt}(875 missing values generated)

{com}.                 egen agree_close = rowtotal(a1c a2c a3c), missing
{txt}(742 missing values generated)

{com}.         *Disagree
.                 gen d1c = d1_disagree * close1
{txt}(746 missing values generated)

{com}.                 gen d2c = d2_disagree * close2
{txt}(793 missing values generated)

{com}.                 gen d3c = d3_disagree * close3
{txt}(875 missing values generated)

{com}.                 egen disagree_close = rowtotal(d1c d2c d3c), missing
{txt}(742 missing values generated)

{com}.         *Scale
.                 gen disagree_total_close = disagree_close - agree_close
{txt}(742 missing values generated)

{com}.                 label var disagree_total_close "Disagreement (Closeness Weighted)"
{txt}
{com}.                 
.                 
.         *Reverse Scaled, so that high = less close*
.                 gen a1c1 = d1_agree * V06P590
{txt}(746 missing values generated)

{com}.                 gen a2c1 = d2_agree * V06P592
{txt}(793 missing values generated)

{com}.                 gen a3c1 = d3_agree * V06P594
{txt}(875 missing values generated)

{com}.                 egen agree_close1 = rowtotal(a1c1 a2c1 a3c1), missing
{txt}(742 missing values generated)

{com}.                 
.                 gen d1c1 = d1_disagree * V06P590
{txt}(746 missing values generated)

{com}.                 gen d2c1 = d2_disagree * V06P592
{txt}(793 missing values generated)

{com}.                 gen d3c1 = d3_disagree * V06P594
{txt}(875 missing values generated)

{com}.                 egen disagree_close1 = rowtotal(d1c1 d2c1 d3c1), missing
{txt}(742 missing values generated)

{com}. 
.                 gen disagree_total_close1 = disagree_close1 - agree_close1
{txt}(742 missing values generated)

{com}.                 label var disagree_total_close1 "Disagreement (Closeness Weighted; Rev)"
{txt}
{com}.                 
.                 
.         
.         
.         
.         
.         
.                 
. *Follow POlitics (2004)
. 
. recode V045095 (1=4) (2=3) (3=2) (4=1) (8=.) (9=.), gen(follow04)
{txt}(1066 differences between V045095 and follow04)

{com}. summ follow04

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 4}follow04 {c |}{res}      1063    2.870179    .9247001          1          4
{txt}
{com}. label var follow04 "Political Interest"
{txt}
{com}. gen follow01 = (follow04 - r(min))/(r(max)-r(min))
{txt}(149 missing values generated)

{com}. label var follow01 "Political Interest"
{txt}
{com}. 
. **Partisan Ambivalence
. recode V043053 (9=.), gen(dem_likes)
{txt}(8 differences between V043053 and dem_likes)

{com}. recode V043055 (9=.), gen(dem_dislikes)
{txt}(5 differences between V043055 and dem_dislikes)

{com}. recode V043057 (9=.), gen(rep_likes)
{txt}(5 differences between V043057 and rep_likes)

{com}. recode V043059 (9=.), gen(rep_dislikes)
{txt}(8 differences between V043059 and rep_dislikes)

{com}. 
.         *ID Consistent
.                 gen consistent = . 
{txt}(1212 missing values generated)

{com}.                 replace consistent = dem_likes + rep_dislikes if pid_204 == 1
{txt}(587 real changes made)

{com}.                 replace consistent = dem_dislikes + rep_likes if pid_204 == 0
{txt}(481 real changes made)

{com}.                 label var consistent "Partisan Identity Consistent Likes/Dislikes"
{txt}
{com}. 
.         *ID Conflicting
.                 gen conflicting = . 
{txt}(1212 missing values generated)

{com}.                 replace conflicting = dem_dislikes + rep_likes if pid_204 == 1
{txt}(589 real changes made)

{com}.                 replace conflicting = dem_likes + rep_dislikes if pid_204 == 0
{txt}(480 real changes made)

{com}.                 label var conflicting "Partisan Identity Conclifting Likes/Dislikes"
{txt}
{com}.         
.         
. *Cognitive Style
. 
.         *need to evaluate
.                 recode V045218 (1=4) (2=3) (3=2) (4=1) (9=.), gen(opinionated)
{txt}(1066 differences between V045218 and opinionated)

{com}.                 rename V045219a opinions
{res}{txt}
{com}.                 
.                 foreach var in opinionated opinions {c -(}
{txt}  2{com}.                         summ `var'
{txt}  3{com}.                         gen `var'01 = (`var' - r(min))/(r(max)-r(min))
{txt}  4{com}.                         {c )-}

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 1}opinionated {c |}{res}      1065    2.746479    .7968154          1          4
{txt}(147 missing values generated)

    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 4}opinions {c |}{res}      1055    3.306161    .8756185          1          5
{txt}(157 missing values generated)

{com}. 
.                 egen evaluate1 = rowtotal(opinionated01 opinions01), missing
{txt}(147 missing values generated)

{com}.                 label var evaluate1 "Need to Evaluate"
{txt}
{com}.                         
.                 
.         *need for cognition
.                 recode V045220a (1=5) (2=4) (3=3) (4=2) (5=1), gen(thinking)
{txt}(703 differences between V045220a and thinking)

{com}.                 recode V045221 (1=0) (5=1) (8=.) (9=.), gen(complex)
{txt}(1066 differences between V045221 and complex)

{com}.                 
.                 summ thinking

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 4}thinking {c |}{res}      1064    3.742481    1.121684          1          5
{txt}
{com}.                 gen thinking01 = (thinking - r(min))/(r(max)-r(min))
{txt}(148 missing values generated)

{com}.         
.                 egen nfc1 = rowtotal(thinking01 complex), missing
{txt}(147 missing values generated)

{com}.                 label var nfc1 "Need for Cognition"
{txt}
{com}.                 
. *for matching
. 
.         
. rename V043250 age2004
{res}{txt}
{com}. label var age2004 "Age (2004)"
{txt}
{com}. 
. 
. tabulate race, gen(rac_)

       {txt}Race {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
      White {c |}{res}        880       73.09       73.09
{txt}      Black {c |}{res}        184       15.28       88.37
{txt}      Other {c |}{res}        140       11.63      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,204      100.00
{txt}
{com}. 
. tabulate pid_str2004, gen(pstr_)

   {txt}PID Str. {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
     Leaner {c |}{res}        348       32.31       32.31
{txt}       Weak {c |}{res}        333       30.92       63.23
{txt}     Strong {c |}{res}        396       36.77      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,077      100.00
{txt}
{com}. 
. 
. 
. 
. *2004 knowledge*
.         foreach var in V045162 V045163 V045164 V045165 V045089 V045090 {c -(} 
{txt}  2{com}.                 codebook `var' 
{txt}  3{com}.                 {c )-}

{txt}{hline}
{res}V045162{right:J7a. Politl knowledge office recognition Dennis Hastert}
{txt}{hline}

{col 19}type:  numeric ({res}byte{txt})
{ralign 22:label}:  {res:V045162f}

{col 18}range:  [{res}1{txt},{res}8{txt}]{col 55}units:  {res}1
{col 10}{txt}unique values:  {res}3{col 51}{txt}missing .:  {res}146{txt}/{res}1212

{txt}{col 13}tabulation:  Freq.   Numeric  Label
{col 24}{res}    117{col 33}       1{col 43}{txt}1. Correctly identifies as
{col 43}Speaker of the House
{col 24}{res}    145{col 33}       5{col 43}{txt}5. Identification is incomplete
{col 43}or wrong
{col 24}{res}    804{col 33}       8{col 43}{txt}8. R makes no attempt to guess
{col 24}{res}    146{col 33}       .{col 43}

{txt}{hline}
{res}V045163{right:J7b. Political knowledge office recognition Dick Cheney}
{txt}{hline}

{col 19}type:  numeric ({res}byte{txt})
{ralign 22:label}:  {res:V045163f}

{col 18}range:  [{res}1{txt},{res}8{txt}]{col 55}units:  {res}1
{col 10}{txt}unique values:  {res}3{col 51}{txt}missing .:  {res}146{txt}/{res}1212

{txt}{col 13}tabulation:  Freq.   Numeric  Label
{col 24}{res}    917{col 33}       1{col 43}{txt}1. Correctly identifies as
{col 43}Vice_President of the U.S.
{col 24}{res}     63{col 33}       5{col 43}{txt}5. Identification is incomplete
{col 43}or wrong
{col 24}{res}     86{col 33}       8{col 43}{txt}8. R makes no attempt to guess
{col 24}{res}    146{col 33}       .{col 43}

{txt}{hline}
{res}V045164{right:J7c. Political knowledge office recognition Tony Blair}
{txt}{hline}

{col 19}type:  numeric ({res}byte{txt})
{ralign 22:label}:  {res:V045164f}

{col 18}range:  [{res}1{txt},{res}8{txt}]{col 55}units:  {res}1
{col 10}{txt}unique values:  {res}3{col 51}{txt}missing .:  {res}146{txt}/{res}1212

{txt}{col 13}tabulation:  Freq.   Numeric  Label
{col 24}{res}    692{col 33}       1{col 43}{txt}1. Correctly identifies as Prime
{col 43}Minister of England/
{col 24}{res}    134{col 33}       5{col 43}{txt}5. Identification is incomplete
{col 43}or wrong
{col 24}{res}    240{col 33}       8{col 43}{txt}8. R makes no attempt to guess
{col 24}{res}    146{col 33}       .{col 43}

{txt}{hline}
{res}V045165{right:J7d. Political knowledge office recognition Wm Rehnquist}
{txt}{hline}

{col 19}type:  numeric ({res}byte{txt})
{ralign 22:label}:  {res:V045165f}

{col 18}range:  [{res}1{txt},{res}8{txt}]{col 55}units:  {res}1
{col 10}{txt}unique values:  {res}3{col 51}{txt}missing .:  {res}146{txt}/{res}1212

{txt}{col 13}tabulation:  Freq.   Numeric  Label
{col 24}{res}    332{col 33}       1{col 43}{txt}1. Correctly identifies as Chief
{col 43}Justice of the Supreme
{col 24}{res}    252{col 33}       5{col 43}{txt}5. Identification is incomplete
{col 43}or wrong
{col 24}{res}    482{col 33}       8{col 43}{txt}8. R makes no attempt to guess
{col 24}{res}    146{col 33}       .{col 43}

{txt}{hline}
{res}V045089{right:E1a. Which party had most members in House prior to electn}
{txt}{hline}

{col 19}type:  numeric ({res}byte{txt})
{ralign 22:label}:  {res:V045089f}

{col 18}range:  [{res}1{txt},{res}8{txt}]{col 55}units:  {res}1
{col 10}{txt}unique values:  {res}3{col 51}{txt}missing .:  {res}146{txt}/{res}1212

{txt}{col 13}tabulation:  Freq.   Numeric  Label
{col 24}{res}    151{col 33}       1{col 43}{txt}1. The Democrats
{col 24}{res}    597{col 33}       5{col 43}{txt}5. The Republicans
{col 24}{res}    318{col 33}       8{col 43}{txt}8. Don't know
{col 24}{res}    146{col 33}       .{col 43}

{txt}{hline}
{res}V045090{right:E1b. Which party had most members in Senate prior to elect}
{txt}{hline}

{col 19}type:  numeric ({res}byte{txt})
{ralign 22:label}:  {res:V045090f}

{col 18}range:  [{res}1{txt},{res}8{txt}]{col 55}units:  {res}1
{col 10}{txt}unique values:  {res}3{col 51}{txt}missing .:  {res}146{txt}/{res}1212

{txt}{col 13}tabulation:  Freq.   Numeric  Label
{col 24}{res}    121{col 33}       1{col 43}{txt}1. The Democrats
{col 24}{res}    540{col 33}       5{col 43}{txt}5. The Republicans
{col 24}{res}    405{col 33}       8{col 43}{txt}8. Don't know
{col 24}{res}    146{col 33}       .{col 43}
{txt}
{com}.                 
.                 label def corre 1 "Correct" 0 "Incorrect/No Guess"
{txt}
{com}.                 recode V045162 (1=1) (5=0) (8=0), gen(hastert)
{txt}(949 differences between V045162 and hastert)

{com}.                 recode V045163 (1=1) (5=0) (8=0), gen(cheney)
{txt}(149 differences between V045163 and cheney)

{com}.                 recode V045164 (1=1) (5=0) (8=0), gen(blair) 
{txt}(374 differences between V045164 and blair)

{com}.                 recode V045165 (1=1) (5=0) (8=0), gen(rein) 
{txt}(734 differences between V045165 and rein)

{com}.                 recode V045089 (1=0) (5=1) (8=0), gen(house)
{txt}(1066 differences between V045089 and house)

{com}.                 recode V045090 (1=0) (5=1) (8=0), gen(senate)
{txt}(1066 differences between V045090 and senate)

{com}. 
.                 foreach var in hastert cheney blair rein house senate {c -(}
{txt}  2{com}.                         label values `var' corre
{txt}  3{com}.                         tab `var'
{txt}  4{com}.                         {c )-}

 {txt}RECODE of V045162 {c |}
      (J7a. Politl {c |}
  knowledge office {c |}
recognition Dennis {c |}
          Hastert) {c |}      Freq.     Percent        Cum.
{hline 19}{c +}{hline 35}
Incorrect/No Guess {c |}{res}        949       89.02       89.02
{txt}           Correct {c |}{res}        117       10.98      100.00
{txt}{hline 19}{c +}{hline 35}
             Total {c |}{res}      1,066      100.00

 {txt}RECODE of V045163 {c |}
   (J7b. Political {c |}
  knowledge office {c |}
  recognition Dick {c |}
           Cheney) {c |}      Freq.     Percent        Cum.
{hline 19}{c +}{hline 35}
Incorrect/No Guess {c |}{res}        149       13.98       13.98
{txt}           Correct {c |}{res}        917       86.02      100.00
{txt}{hline 19}{c +}{hline 35}
             Total {c |}{res}      1,066      100.00

 {txt}RECODE of V045164 {c |}
   (J7c. Political {c |}
  knowledge office {c |}
  recognition Tony {c |}
            Blair) {c |}      Freq.     Percent        Cum.
{hline 19}{c +}{hline 35}
Incorrect/No Guess {c |}{res}        374       35.08       35.08
{txt}           Correct {c |}{res}        692       64.92      100.00
{txt}{hline 19}{c +}{hline 35}
             Total {c |}{res}      1,066      100.00

 {txt}RECODE of V045165 {c |}
   (J7d. Political {c |}
  knowledge office {c |}
    recognition Wm {c |}
        Rehnquist) {c |}      Freq.     Percent        Cum.
{hline 19}{c +}{hline 35}
Incorrect/No Guess {c |}{res}        734       68.86       68.86
{txt}           Correct {c |}{res}        332       31.14      100.00
{txt}{hline 19}{c +}{hline 35}
             Total {c |}{res}      1,066      100.00

 {txt}RECODE of V045089 {c |}
 (E1a. Which party {c |}
  had most members {c |}
 in House prior to {c |}
           electn) {c |}      Freq.     Percent        Cum.
{hline 19}{c +}{hline 35}
Incorrect/No Guess {c |}{res}        469       44.00       44.00
{txt}           Correct {c |}{res}        597       56.00      100.00
{txt}{hline 19}{c +}{hline 35}
             Total {c |}{res}      1,066      100.00

 {txt}RECODE of V045090 {c |}
 (E1b. Which party {c |}
  had most members {c |}
in Senate prior to {c |}
            elect) {c |}      Freq.     Percent        Cum.
{hline 19}{c +}{hline 35}
Incorrect/No Guess {c |}{res}        526       49.34       49.34
{txt}           Correct {c |}{res}        540       50.66      100.00
{txt}{hline 19}{c +}{hline 35}
             Total {c |}{res}      1,066      100.00
{txt}
{com}.                 egen knowl04 = rowtotal(hastert cheney blair rein house senate), missing
{txt}(146 missing values generated)

{com}.                 tab knowl04

    {txt}knowl04 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        103        9.66        9.66
{txt}          1 {c |}{res}        140       13.13       22.80
{txt}          2 {c |}{res}        187       17.54       40.34
{txt}          3 {c |}{res}        191       17.92       58.26
{txt}          4 {c |}{res}        199       18.67       76.92
{txt}          5 {c |}{res}        164       15.38       92.31
{txt}          6 {c |}{res}         82        7.69      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,066      100.00
{txt}
{com}.                 summ knowl04, detail

                           {txt}knowl04
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        0              0
{txt}10%    {res}        1              0       {txt}Obs         {res}       1066
{txt}25%    {res}        2              0       {txt}Sum of Wgt. {res}       1066

{txt}50%    {res}        3                      {txt}Mean          {res} 2.997186
                        {txt}Largest       Std. Dev.     {res} 1.751456
{txt}75%    {res}        4              6
{txt}90%    {res}        5              6       {txt}Variance      {res} 3.067598
{txt}95%    {res}        6              6       {txt}Skewness      {res}-.0586465
{txt}99%    {res}        6              6       {txt}Kurtosis      {res} 2.020598
{txt}
{com}.                 label var knowl04 "Political Knowledge (2004)"
{txt}
{com}. 
. 
. summ interest

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 4}interest {c |}{res}       674    9.01e-09    .9992568  -2.449235   1.833793
{txt}
{com}. gen interest01 = (interest - r(min))/(r(max)-r(min))
{txt}(538 missing values generated)

{com}. label var interest01 "Political Interest"
{txt}
{com}. 
. /*******************************************Leeaners as Ind.*******************************/
. 
. 
. 
. gen d1_agree_nol = . 
{txt}(1212 missing values generated)

{com}.                 replace d1_agree_nol = 1 if pid_3 == 1 & V06P603x >=0 & V06P603x <=1
{txt}(153 real changes made)

{com}.                 replace d1_agree_nol = 1 if pid_3 == 2 & V06P603x >=5 & V06P603x <=6 
{txt}(129 real changes made)

{com}.                 replace d1_agree_nol = 1 if pid_3 == 3 & V06P603x == 3
{txt}(8 real changes made)

{com}. 
.                 replace d1_agree_nol = 0 if pid_3 == 1 & V06P603x >=2 & V06P603x <=6
{txt}(90 real changes made)

{com}.                 replace d1_agree_nol = 0 if pid_3 == 2 & V06P603x >=0 & V06P603x <=4
{txt}(75 real changes made)

{com}.                 replace d1_agree_nol = 0 if pid_3 == 3 & V06P603x >=0 & V06P603x <=2
{txt}(5 real changes made)

{com}.                 replace d1_agree_nol = 0 if pid_3 == 3 & V06P603x >=4 & V06P603x <=6
{txt}(6 real changes made)

{com}.                 label var d1_agree_nol "Agree with D1?"
{txt}
{com}.                 label values d1_agree_nol agr
{txt}
{com}.                 
.                 gen d2_agree_nol = . 
{txt}(1212 missing values generated)

{com}.                 replace d2_agree_nol = 1 if pid_3 == 1 & V06P608x >=0 & V06P608x <=1
{txt}(142 real changes made)

{com}.                 replace d2_agree_nol = 1 if pid_3 == 2 & V06P608x >=5 & V06P608x <=6 
{txt}(109 real changes made)

{com}.                 
.                 replace d2_agree_nol = 0 if pid_3 == 1 & V06P608x >=2 & V06P608x <=6
{txt}(77 real changes made)

{com}.                 replace d2_agree_nol = 0 if pid_3 == 2 & V06P608x >=0 & V06P608x <=4
{txt}(77 real changes made)

{com}. 
.                 replace d2_agree_nol = 1 if pid_3 == 3 & V06P608x == 3
{txt}(5 real changes made)

{com}.                 replace d2_agree_nol = 0 if pid_3 == 3 & V06P608x >=0 & V06P608x <=2
{txt}(5 real changes made)

{com}.                 replace d2_agree_nol = 0 if pid_3 == 3 & V06P608x >=4 & V06P608x <=6
{txt}(5 real changes made)

{com}.                 
.                 label var d2_agree_nol "Agree with D2?"
{txt}
{com}.                 label values d2_agree_nol agr
{txt}
{com}.                 
.                 gen d3_agree_nol = . 
{txt}(1212 missing values generated)

{com}.                 replace d3_agree_nol = 1 if pid_3 == 1 & V06P613x >=0 & V06P613x <=1
{txt}(109 real changes made)

{com}.                 replace d3_agree_nol = 1 if pid_3 == 2 & V06P613x >=5 & V06P613x <=6 
{txt}(88 real changes made)

{com}.                 replace d3_agree_nol = 0 if pid_3 == 1 & V06P613x >=2 & V06P613x <=6
{txt}(66 real changes made)

{com}.                 replace d3_agree_nol = 0 if pid_3 == 2 & V06P613x >=0 & V06P613x <=4
{txt}(63 real changes made)

{com}. 
.                 replace d3_agree_nol = 1 if pid_3 == 3 & V06P613x == 3
{txt}(1 real change made)

{com}.                 replace d3_agree_nol = 0 if pid_3 == 3 & V06P613x >=0 & V06P613x <=2
{txt}(7 real changes made)

{com}.                 replace d3_agree_nol = 0 if pid_3 == 3 & V06P613x >=4 & V06P613x <=6
{txt}(3 real changes made)

{com}.                 
.                 label var d3_agree_nol "Agree with D3?"
{txt}
{com}.                 label values d3_agree_nol agr
{txt}
{com}. 
.                 
.                 
.                 *Number Disagreeble
.                 foreach var in d1_agree_nol d2_agree_nol  d3_agree_nol {c -(}
{txt}  2{com}.                         omscore `var'
{txt}  3{com}.                         {c )-}
{txt}Reverse recoding ok. New variabele = rr_d1_agree_nol
Reverse recoding ok. New variabele = rr_d2_agree_nol
Reverse recoding ok. New variabele = rr_d3_agree_nol

{com}.                         
.                 rename rr_d1_agree_nol d1_disagree_nol
{res}{txt}
{com}.                 rename rr_d2_agree_nol d2_disagree_nol
{res}{txt}
{com}.                 rename rr_d3_agree_nol d3_disagree_nol
{res}{txt}
{com}. 
.         
.                 label var d1_disagree_nol "Disagree with D1?"
{txt}
{com}.                 label values d1_disagree_nol dagr
{txt}
{com}.                         
.                 label var d2_disagree_nol "Agree with D2?"
{txt}
{com}.                 label values d2_disagree_nol dagr
{txt}
{com}.                                 
.                 label var d3_disagree_nol "Agree with D3?"
{txt}
{com}.                 label values d3_disagree_nol dagr
{txt}
{com}.         
. *Summary and Average
.         *Summary Agreement
.                 egen pid_agree_nl = rowtotal(d1_agree_nol d2_agree_nol d3_agree_nol), missing
{txt}(742 missing values generated)

{com}.         *Disagreement
.                 egen pid_disagree_nl = rowtotal(d1_disagree_nol d2_disagree_nol d3_disagree_nol), missing
{txt}(742 missing values generated)

{com}.         
.         *Summary Scale
.                         *See Lupton and Thornton: Exposure = D - A
.                         gen disagree_total_nl = pid_disagree_nl - pid_agree_nl
{txt}(742 missing values generated)

{com}.                         label var disagree_total_nl "Network Disagreement"
{txt}
{com}. 
{txt}end of do-file

{com}. set more off
{txt}
{com}. 
. 
. /***Partisan Extremity***/
. eststo clear
{txt}
{com}. foreach var in disagree_total disagree_avg disagree_total_freq disagree_total_int ///
>                         disagree_total_close disagree_total_weight disc_gen {c -(}
{txt}  2{com}.         eststo: ologit pid_str_full `var' names disc_interest i.pid_204 follow04   ///
>                         i.gender i.race age2004 educ income i.marital evaluate1 nfc1 [pweight = V06P002]
{txt}  3{com}.         {c )-}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-392.77652}  
Iteration 1:{space 3}log pseudolikelihood = {res:-373.06994}  
Iteration 2:{space 3}log pseudolikelihood = {res:-372.87821}  
Iteration 3:{space 3}log pseudolikelihood = {res:-372.87811}  
Iteration 4:{space 3}log pseudolikelihood = {res:-372.87811}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       393
{txt}{col 51}Wald chi2({res}14{txt}){col 67}= {res}     36.83
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0008
{txt}Log pseudolikelihood = {res}-372.87811{txt}{col 51}Pseudo R2{col 67}= {res}    0.0507

{txt}{hline 15}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 16}{c |}{col 28}    Robust
{col 1}  pid_str_full{col 16}{c |}      Coef.{col 28}   Std. Err.{col 40}      z{col 48}   P>|z|{col 56}     [95% Con{col 69}f. Interval]
{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
disagree_total {c |}{col 16}{res}{space 2}-.2365068{col 28}{space 2} .0944354{col 39}{space 1}   -2.50{col 48}{space 3}0.012{col 56}{space 4}-.4215969{col 69}{space 3}-.0514168
{txt}{space 9}names {c |}{col 16}{res}{space 2}-.2960149{col 28}{space 2} .2131625{col 39}{space 1}   -1.39{col 48}{space 3}0.165{col 56}{space 4}-.7138057{col 69}{space 3} .1217759
{txt}{space 1}disc_interest {c |}{col 16}{res}{space 2} .0450719{col 28}{space 2} .1827069{col 39}{space 1}    0.25{col 48}{space 3}0.805{col 56}{space 4} -.313027{col 69}{space 3} .4031708
{txt}{space 14} {c |}
{space 7}pid_204 {c |}
{space 5}Democrat  {c |}{col 16}{res}{space 2} .0841621{col 28}{space 2} .2708239{col 39}{space 1}    0.31{col 48}{space 3}0.756{col 56}{space 4} -.446643{col 69}{space 3} .6149672
{txt}{space 6}follow04 {c |}{col 16}{res}{space 2}  .100646{col 28}{space 2} .1483194{col 39}{space 1}    0.68{col 48}{space 3}0.497{col 56}{space 4}-.1900547{col 69}{space 3} .3913466
{txt}{space 14} {c |}
{space 8}gender {c |}
{space 7}Female  {c |}{col 16}{res}{space 2} .4632993{col 28}{space 2} .2658067{col 39}{space 1}    1.74{col 48}{space 3}0.081{col 56}{space 4}-.0576723{col 69}{space 3} .9842709
{txt}{space 14} {c |}
{space 10}race {c |}
{space 8}Black  {c |}{col 16}{res}{space 2} .3360439{col 28}{space 2}  .379701{col 39}{space 1}    0.89{col 48}{space 3}0.376{col 56}{space 4}-.4081564{col 69}{space 3} 1.080244
{txt}{space 8}Other  {c |}{col 16}{res}{space 2}-.0445283{col 28}{space 2} .4431329{col 39}{space 1}   -0.10{col 48}{space 3}0.920{col 56}{space 4}-.9130528{col 69}{space 3} .8239962
{txt}{space 14} {c |}
{space 7}age2004 {c |}{col 16}{res}{space 2} .0104779{col 28}{space 2} .0085912{col 39}{space 1}    1.22{col 48}{space 3}0.223{col 56}{space 4}-.0063606{col 69}{space 3} .0273164
{txt}{space 10}educ {c |}{col 16}{res}{space 2}-.2834809{col 28}{space 2} .1524651{col 39}{space 1}   -1.86{col 48}{space 3}0.063{col 56}{space 4}-.5823069{col 69}{space 3} .0153451
{txt}{space 8}income {c |}{col 16}{res}{space 2}-.0125818{col 28}{space 2}  .028401{col 39}{space 1}   -0.44{col 48}{space 3}0.658{col 56}{space 4}-.0682467{col 69}{space 3} .0430832
{txt}{space 14} {c |}
{space 7}marital {c |}
{space 6}Married  {c |}{col 16}{res}{space 2} .1498521{col 28}{space 2} .2910383{col 39}{space 1}    0.51{col 48}{space 3}0.607{col 56}{space 4}-.4205725{col 69}{space 3} .7202766
{txt}{space 5}evaluate1 {c |}{col 16}{res}{space 2}  .267243{col 28}{space 2} .3174397{col 39}{space 1}    0.84{col 48}{space 3}0.400{col 56}{space 4}-.3549273{col 69}{space 3} .8894134
{txt}{space 10}nfc1 {c |}{col 16}{res}{space 2}-.1663538{col 28}{space 2} .1986068{col 39}{space 1}   -0.84{col 48}{space 3}0.402{col 56}{space 4}-.5556159{col 69}{space 3} .2229083
{txt}{hline 15}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
         /cut1 {c |}{col 16}{res}{space 2}-4.133199{col 28}{space 2}  1.28063{col 56}{space 4}-6.643187{col 69}{space 3} -1.62321
{txt}         /cut2 {c |}{col 16}{res}{space 2}-1.487476{col 28}{space 2} 1.273291{col 56}{space 4} -3.98308{col 69}{space 3} 1.008127
{txt}         /cut3 {c |}{col 16}{res}{space 2}  -.24053{col 28}{space 2} 1.291818{col 56}{space 4}-2.772446{col 69}{space 3} 2.291386
{txt}{hline 15}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-392.77652}  
Iteration 1:{space 3}log pseudolikelihood = {res:-375.33587}  
Iteration 2:{space 3}log pseudolikelihood = {res:-375.18549}  
Iteration 3:{space 3}log pseudolikelihood = {res:-375.18543}  
Iteration 4:{space 3}log pseudolikelihood = {res:-375.18543}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       393
{txt}{col 51}Wald chi2({res}14{txt}){col 67}= {res}     32.26
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0037
{txt}Log pseudolikelihood = {res}-375.18543{txt}{col 51}Pseudo R2{col 67}= {res}    0.0448

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1} pid_str_full{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}disagree_avg {c |}{col 15}{res}{space 2}-.4686195{col 27}{space 2} .2460146{col 38}{space 1}   -1.90{col 47}{space 3}0.057{col 55}{space 4}-.9507992{col 68}{space 3} .0135602
{txt}{space 8}names {c |}{col 15}{res}{space 2}-.1925218{col 27}{space 2} .2195669{col 38}{space 1}   -0.88{col 47}{space 3}0.381{col 55}{space 4} -.622865{col 68}{space 3} .2378214
{txt}disc_interest {c |}{col 15}{res}{space 2}  .077711{col 27}{space 2} .1828622{col 38}{space 1}    0.42{col 47}{space 3}0.671{col 55}{space 4}-.2806922{col 68}{space 3} .4361143
{txt}{space 13} {c |}
{space 6}pid_204 {c |}
{space 4}Democrat  {c |}{col 15}{res}{space 2} .1105665{col 27}{space 2} .2687985{col 38}{space 1}    0.41{col 47}{space 3}0.681{col 55}{space 4}-.4162689{col 68}{space 3}  .637402
{txt}{space 5}follow04 {c |}{col 15}{res}{space 2} .0945463{col 27}{space 2} .1452169{col 38}{space 1}    0.65{col 47}{space 3}0.515{col 55}{space 4}-.1900737{col 68}{space 3} .3791662
{txt}{space 13} {c |}
{space 7}gender {c |}
{space 6}Female  {c |}{col 15}{res}{space 2} .4986174{col 27}{space 2} .2609356{col 38}{space 1}    1.91{col 47}{space 3}0.056{col 55}{space 4}-.0128069{col 68}{space 3} 1.010042
{txt}{space 13} {c |}
{space 9}race {c |}
{space 7}Black  {c |}{col 15}{res}{space 2} .3473876{col 27}{space 2}  .369864{col 38}{space 1}    0.94{col 47}{space 3}0.348{col 55}{space 4}-.3775324{col 68}{space 3} 1.072308
{txt}{space 7}Other  {c |}{col 15}{res}{space 2}-.0797709{col 27}{space 2} .4409772{col 38}{space 1}   -0.18{col 47}{space 3}0.856{col 55}{space 4}-.9440703{col 68}{space 3} .7845286
{txt}{space 13} {c |}
{space 6}age2004 {c |}{col 15}{res}{space 2} .0097868{col 27}{space 2} .0085117{col 38}{space 1}    1.15{col 47}{space 3}0.250{col 55}{space 4}-.0068959{col 68}{space 3} .0264695
{txt}{space 9}educ {c |}{col 15}{res}{space 2}-.2884498{col 27}{space 2}  .150074{col 38}{space 1}   -1.92{col 47}{space 3}0.055{col 55}{space 4}-.5825893{col 68}{space 3} .0056898
{txt}{space 7}income {c |}{col 15}{res}{space 2}-.0088675{col 27}{space 2} .0285128{col 38}{space 1}   -0.31{col 47}{space 3}0.756{col 55}{space 4}-.0647515{col 68}{space 3} .0470165
{txt}{space 13} {c |}
{space 6}marital {c |}
{space 5}Married  {c |}{col 15}{res}{space 2} .1381571{col 27}{space 2} .2908308{col 38}{space 1}    0.48{col 47}{space 3}0.635{col 55}{space 4}-.4318607{col 68}{space 3} .7081749
{txt}{space 4}evaluate1 {c |}{col 15}{res}{space 2} .2942458{col 27}{space 2} .3132748{col 38}{space 1}    0.94{col 47}{space 3}0.348{col 55}{space 4}-.3197615{col 68}{space 3} .9082531
{txt}{space 9}nfc1 {c |}{col 15}{res}{space 2}-.1815117{col 27}{space 2} .1986651{col 38}{space 1}   -0.91{col 47}{space 3}0.361{col 55}{space 4}-.5708881{col 68}{space 3} .2078647
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
        /cut1 {c |}{col 15}{res}{space 2}-3.754323{col 27}{space 2} 1.306443{col 55}{space 4}-6.314905{col 68}{space 3} -1.19374
{txt}        /cut2 {c |}{col 15}{res}{space 2}-1.125125{col 27}{space 2} 1.294056{col 55}{space 4}-3.661428{col 68}{space 3} 1.411179
{txt}        /cut3 {c |}{col 15}{res}{space 2} .1099311{col 27}{space 2} 1.312551{col 55}{space 4}-2.462622{col 68}{space 3} 2.682485
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-392.77652}  
Iteration 1:{space 3}log pseudolikelihood = {res:-370.97851}  
Iteration 2:{space 3}log pseudolikelihood = {res:-370.71496}  
Iteration 3:{space 3}log pseudolikelihood = {res:-370.71473}  
Iteration 4:{space 3}log pseudolikelihood = {res:-370.71473}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       393
{txt}{col 51}Wald chi2({res}14{txt}){col 67}= {res}     36.44
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0009
{txt}Log pseudolikelihood = {res}-370.71473{txt}{col 51}Pseudo R2{col 67}= {res}    0.0562

{txt}{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}       pid_str_full{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
disagree_total_freq {c |}{col 21}{res}{space 2} -.079974{col 33}{space 2} .0249692{col 44}{space 1}   -3.20{col 53}{space 3}0.001{col 61}{space 4}-.1289128{col 74}{space 3}-.0310352
{txt}{space 14}names {c |}{col 21}{res}{space 2}-.2771682{col 33}{space 2} .2123712{col 44}{space 1}   -1.31{col 53}{space 3}0.192{col 61}{space 4}-.6934081{col 74}{space 3} .1390717
{txt}{space 6}disc_interest {c |}{col 21}{res}{space 2} .0270146{col 33}{space 2} .1837236{col 44}{space 1}    0.15{col 53}{space 3}0.883{col 61}{space 4}-.3330771{col 74}{space 3} .3871062
{txt}{space 19} {c |}
{space 12}pid_204 {c |}
{space 10}Democrat  {c |}{col 21}{res}{space 2} .0435241{col 33}{space 2} .2707039{col 44}{space 1}    0.16{col 53}{space 3}0.872{col 61}{space 4}-.4870457{col 74}{space 3} .5740939
{txt}{space 11}follow04 {c |}{col 21}{res}{space 2} .1034345{col 33}{space 2} .1540197{col 44}{space 1}    0.67{col 53}{space 3}0.502{col 61}{space 4}-.1984385{col 74}{space 3} .4053075
{txt}{space 19} {c |}
{space 13}gender {c |}
{space 12}Female  {c |}{col 21}{res}{space 2} .4039424{col 33}{space 2} .2678618{col 44}{space 1}    1.51{col 53}{space 3}0.132{col 61}{space 4}-.1210569{col 74}{space 3} .9289418
{txt}{space 19} {c |}
{space 15}race {c |}
{space 13}Black  {c |}{col 21}{res}{space 2} .3645945{col 33}{space 2} .3753356{col 44}{space 1}    0.97{col 53}{space 3}0.331{col 61}{space 4}-.3710498{col 74}{space 3} 1.100239
{txt}{space 13}Other  {c |}{col 21}{res}{space 2}-.0582926{col 33}{space 2}  .421419{col 44}{space 1}   -0.14{col 53}{space 3}0.890{col 61}{space 4}-.8842586{col 74}{space 3} .7676735
{txt}{space 19} {c |}
{space 12}age2004 {c |}{col 21}{res}{space 2} .0130198{col 33}{space 2} .0086619{col 44}{space 1}    1.50{col 53}{space 3}0.133{col 61}{space 4}-.0039572{col 74}{space 3} .0299968
{txt}{space 15}educ {c |}{col 21}{res}{space 2}-.2889934{col 33}{space 2} .1512349{col 44}{space 1}   -1.91{col 53}{space 3}0.056{col 61}{space 4}-.5854083{col 74}{space 3} .0074215
{txt}{space 13}income {c |}{col 21}{res}{space 2}-.0122267{col 33}{space 2} .0283078{col 44}{space 1}   -0.43{col 53}{space 3}0.666{col 61}{space 4}-.0677089{col 74}{space 3} .0432555
{txt}{space 19} {c |}
{space 12}marital {c |}
{space 11}Married  {c |}{col 21}{res}{space 2} .1676968{col 33}{space 2} .2883517{col 44}{space 1}    0.58{col 53}{space 3}0.561{col 61}{space 4}-.3974622{col 74}{space 3} .7328558
{txt}{space 10}evaluate1 {c |}{col 21}{res}{space 2} .2780939{col 33}{space 2} .3220252{col 44}{space 1}    0.86{col 53}{space 3}0.388{col 61}{space 4} -.353064{col 74}{space 3} .9092517
{txt}{space 15}nfc1 {c |}{col 21}{res}{space 2}-.1372458{col 33}{space 2} .1962525{col 44}{space 1}   -0.70{col 53}{space 3}0.484{col 61}{space 4}-.5218936{col 74}{space 3} .2474019
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
              /cut1 {c |}{col 21}{res}{space 2}-3.995735{col 33}{space 2} 1.297237{col 61}{space 4}-6.538272{col 74}{space 3}-1.453198
{txt}              /cut2 {c |}{col 21}{res}{space 2}-1.337922{col 33}{space 2}  1.27994{col 61}{space 4}-3.846559{col 74}{space 3} 1.170714
{txt}              /cut3 {c |}{col 21}{res}{space 2}-.0781328{col 33}{space 2} 1.296442{col 61}{space 4}-2.619112{col 74}{space 3} 2.462847
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-392.77652}  
Iteration 1:{space 3}log pseudolikelihood = {res:-368.42854}  
Iteration 2:{space 3}log pseudolikelihood = {res:-368.10618}  
Iteration 3:{space 3}log pseudolikelihood = {res:-368.10585}  
Iteration 4:{space 3}log pseudolikelihood = {res:-368.10585}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       393
{txt}{col 51}Wald chi2({res}14{txt}){col 67}= {res}     42.85
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0001
{txt}Log pseudolikelihood = {res}-368.10585{txt}{col 51}Pseudo R2{col 67}= {res}    0.0628

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}      pid_str_full{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      z{col 52}   P>|z|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
disagree_total_int {c |}{col 20}{res}{space 2}-.0900374{col 32}{space 2}  .024892{col 43}{space 1}   -3.62{col 52}{space 3}0.000{col 60}{space 4}-.1388249{col 73}{space 3}-.0412499
{txt}{space 13}names {c |}{col 20}{res}{space 2}-.3469368{col 32}{space 2} .2163048{col 43}{space 1}   -1.60{col 52}{space 3}0.109{col 60}{space 4}-.7708865{col 73}{space 3} .0770128
{txt}{space 5}disc_interest {c |}{col 20}{res}{space 2}-.0715577{col 32}{space 2} .1881345{col 43}{space 1}   -0.38{col 52}{space 3}0.704{col 60}{space 4}-.4402945{col 73}{space 3} .2971792
{txt}{space 18} {c |}
{space 11}pid_204 {c |}
{space 9}Democrat  {c |}{col 20}{res}{space 2}  .074823{col 32}{space 2} .2696667{col 43}{space 1}    0.28{col 52}{space 3}0.781{col 60}{space 4}-.4537141{col 73}{space 3} .6033601
{txt}{space 10}follow04 {c |}{col 20}{res}{space 2} .0859481{col 32}{space 2} .1490601{col 43}{space 1}    0.58{col 52}{space 3}0.564{col 60}{space 4}-.2062044{col 73}{space 3} .3781005
{txt}{space 18} {c |}
{space 12}gender {c |}
{space 11}Female  {c |}{col 20}{res}{space 2} .4633899{col 32}{space 2} .2633355{col 43}{space 1}    1.76{col 52}{space 3}0.078{col 60}{space 4}-.0527383{col 73}{space 3} .9795181
{txt}{space 18} {c |}
{space 14}race {c |}
{space 12}Black  {c |}{col 20}{res}{space 2} .3101022{col 32}{space 2} .3865593{col 43}{space 1}    0.80{col 52}{space 3}0.422{col 60}{space 4}-.4475401{col 73}{space 3} 1.067744
{txt}{space 12}Other  {c |}{col 20}{res}{space 2}-.0165127{col 32}{space 2} .4411769{col 43}{space 1}   -0.04{col 52}{space 3}0.970{col 60}{space 4}-.8812036{col 73}{space 3} .8481781
{txt}{space 18} {c |}
{space 11}age2004 {c |}{col 20}{res}{space 2} .0119745{col 32}{space 2} .0085364{col 43}{space 1}    1.40{col 52}{space 3}0.161{col 60}{space 4}-.0047566{col 73}{space 3} .0287056
{txt}{space 14}educ {c |}{col 20}{res}{space 2}-.2741596{col 32}{space 2} .1533493{col 43}{space 1}   -1.79{col 52}{space 3}0.074{col 60}{space 4}-.5747187{col 73}{space 3} .0263995
{txt}{space 12}income {c |}{col 20}{res}{space 2} -.016655{col 32}{space 2} .0281526{col 43}{space 1}   -0.59{col 52}{space 3}0.554{col 60}{space 4}-.0718332{col 73}{space 3} .0385231
{txt}{space 18} {c |}
{space 11}marital {c |}
{space 10}Married  {c |}{col 20}{res}{space 2} .1644314{col 32}{space 2} .2882826{col 43}{space 1}    0.57{col 52}{space 3}0.568{col 60}{space 4}-.4005921{col 73}{space 3} .7294549
{txt}{space 9}evaluate1 {c |}{col 20}{res}{space 2} .2993222{col 32}{space 2} .3157474{col 43}{space 1}    0.95{col 52}{space 3}0.343{col 60}{space 4}-.3195314{col 73}{space 3} .9181758
{txt}{space 14}nfc1 {c |}{col 20}{res}{space 2}-.1605943{col 32}{space 2} .1980863{col 43}{space 1}   -0.81{col 52}{space 3}0.418{col 60}{space 4}-.5488362{col 73}{space 3} .2276476
{txt}{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
             /cut1 {c |}{col 20}{res}{space 2}-4.596237{col 32}{space 2} 1.319311{col 60}{space 4}-7.182039{col 73}{space 3}-2.010435
{txt}             /cut2 {c |}{col 20}{res}{space 2}-1.906135{col 32}{space 2} 1.310722{col 60}{space 4}-4.475102{col 73}{space 3} .6628326
{txt}             /cut3 {c |}{col 20}{res}{space 2}-.6361882{col 32}{space 2} 1.326211{col 60}{space 4}-3.235514{col 73}{space 3} 1.963137
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-392.77652}  
Iteration 1:{space 3}log pseudolikelihood = {res:-371.85473}  
Iteration 2:{space 3}log pseudolikelihood = {res: -371.6316}  
Iteration 3:{space 3}log pseudolikelihood = {res:-371.63146}  
Iteration 4:{space 3}log pseudolikelihood = {res:-371.63146}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       393
{txt}{col 51}Wald chi2({res}14{txt}){col 67}= {res}     36.50
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0009
{txt}Log pseudolikelihood = {res}-371.63146{txt}{col 51}Pseudo R2{col 67}= {res}    0.0538

{txt}{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}        pid_str_full{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
disagree_total_close {c |}{col 22}{res}{space 2}-.0627258{col 34}{space 2} .0219379{col 45}{space 1}   -2.86{col 54}{space 3}0.004{col 62}{space 4}-.1057233{col 75}{space 3}-.0197284
{txt}{space 15}names {c |}{col 22}{res}{space 2}-.2921317{col 34}{space 2} .2120811{col 45}{space 1}   -1.38{col 54}{space 3}0.168{col 62}{space 4} -.707803{col 75}{space 3} .1235397
{txt}{space 7}disc_interest {c |}{col 22}{res}{space 2} .0449787{col 34}{space 2} .1838805{col 45}{space 1}    0.24{col 54}{space 3}0.807{col 62}{space 4}-.3154204{col 75}{space 3} .4053777
{txt}{space 20} {c |}
{space 13}pid_204 {c |}
{space 11}Democrat  {c |}{col 22}{res}{space 2} .0376102{col 34}{space 2}  .273861{col 45}{space 1}    0.14{col 54}{space 3}0.891{col 62}{space 4}-.4991475{col 75}{space 3} .5743678
{txt}{space 12}follow04 {c |}{col 22}{res}{space 2}  .100253{col 34}{space 2} .1499605{col 45}{space 1}    0.67{col 54}{space 3}0.504{col 62}{space 4}-.1936643{col 75}{space 3} .3941702
{txt}{space 20} {c |}
{space 14}gender {c |}
{space 13}Female  {c |}{col 22}{res}{space 2} .4313244{col 34}{space 2} .2688781{col 45}{space 1}    1.60{col 54}{space 3}0.109{col 62}{space 4} -.095667{col 75}{space 3} .9583159
{txt}{space 20} {c |}
{space 16}race {c |}
{space 14}Black  {c |}{col 22}{res}{space 2} .4043733{col 34}{space 2} .3943415{col 45}{space 1}    1.03{col 54}{space 3}0.305{col 62}{space 4}-.3685217{col 75}{space 3} 1.177268
{txt}{space 14}Other  {c |}{col 22}{res}{space 2}-.0194163{col 34}{space 2} .4387246{col 45}{space 1}   -0.04{col 54}{space 3}0.965{col 62}{space 4}-.8793008{col 75}{space 3} .8404681
{txt}{space 20} {c |}
{space 13}age2004 {c |}{col 22}{res}{space 2} .0105353{col 34}{space 2} .0084807{col 45}{space 1}    1.24{col 54}{space 3}0.214{col 62}{space 4}-.0060866{col 75}{space 3} .0271572
{txt}{space 16}educ {c |}{col 22}{res}{space 2}-.2732868{col 34}{space 2} .1537362{col 45}{space 1}   -1.78{col 54}{space 3}0.075{col 62}{space 4}-.5746042{col 75}{space 3} .0280307
{txt}{space 14}income {c |}{col 22}{res}{space 2}-.0120201{col 34}{space 2} .0283826{col 45}{space 1}   -0.42{col 54}{space 3}0.672{col 62}{space 4} -.067649{col 75}{space 3} .0436088
{txt}{space 20} {c |}
{space 13}marital {c |}
{space 12}Married  {c |}{col 22}{res}{space 2} .1575633{col 34}{space 2}   .29091{col 45}{space 1}    0.54{col 54}{space 3}0.588{col 62}{space 4}-.4126098{col 75}{space 3} .7277364
{txt}{space 11}evaluate1 {c |}{col 22}{res}{space 2} .2567668{col 34}{space 2} .3176925{col 45}{space 1}    0.81{col 54}{space 3}0.419{col 62}{space 4} -.365899{col 75}{space 3} .8794326
{txt}{space 16}nfc1 {c |}{col 22}{res}{space 2}-.1711379{col 34}{space 2} .1966353{col 45}{space 1}   -0.87{col 54}{space 3}0.384{col 62}{space 4}-.5565359{col 75}{space 3} .2142601
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
               /cut1 {c |}{col 22}{res}{space 2}-4.101748{col 34}{space 2} 1.297205{col 62}{space 4}-6.644223{col 75}{space 3}-1.559273
{txt}               /cut2 {c |}{col 22}{res}{space 2} -1.44291{col 34}{space 2} 1.286736{col 62}{space 4}-3.964866{col 75}{space 3} 1.079045
{txt}               /cut3 {c |}{col 22}{res}{space 2}-.1895994{col 34}{space 2}  1.30542{col 62}{space 4}-2.748176{col 75}{space 3} 2.368977
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est5{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-392.77652}  
Iteration 1:{space 3}log pseudolikelihood = {res:-372.70625}  
Iteration 2:{space 3}log pseudolikelihood = {res:-372.48551}  
Iteration 3:{space 3}log pseudolikelihood = {res:-372.48535}  
Iteration 4:{space 3}log pseudolikelihood = {res:-372.48535}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       393
{txt}{col 51}Wald chi2({res}14{txt}){col 67}= {res}     36.75
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0008
{txt}Log pseudolikelihood = {res}-372.48535{txt}{col 51}Pseudo R2{col 67}= {res}    0.0517

{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}    Robust
{col 1}         pid_str_full{col 23}{c |}      Coef.{col 35}   Std. Err.{col 47}      z{col 55}   P>|z|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
disagree_total_weight {c |}{col 23}{res}{space 2}-.0648456{col 35}{space 2} .0236702{col 46}{space 1}   -2.74{col 55}{space 3}0.006{col 63}{space 4}-.1112383{col 76}{space 3}-.0184529
{txt}{space 16}names {c |}{col 23}{res}{space 2}-.3194032{col 35}{space 2} .2148544{col 46}{space 1}   -1.49{col 55}{space 3}0.137{col 63}{space 4}-.7405101{col 76}{space 3} .1017036
{txt}{space 8}disc_interest {c |}{col 23}{res}{space 2} .0380018{col 35}{space 2} .1826438{col 46}{space 1}    0.21{col 55}{space 3}0.835{col 63}{space 4}-.3199735{col 76}{space 3} .3959772
{txt}{space 21} {c |}
{space 14}pid_204 {c |}
{space 12}Democrat  {c |}{col 23}{res}{space 2} .0971348{col 35}{space 2}  .267331{col 46}{space 1}    0.36{col 55}{space 3}0.716{col 63}{space 4}-.4268244{col 76}{space 3}  .621094
{txt}{space 13}follow04 {c |}{col 23}{res}{space 2} .0751356{col 35}{space 2}  .145464{col 46}{space 1}    0.52{col 55}{space 3}0.605{col 63}{space 4}-.2099686{col 76}{space 3} .3602397
{txt}{space 21} {c |}
{space 15}gender {c |}
{space 14}Female  {c |}{col 23}{res}{space 2} .4483929{col 35}{space 2} .2627417{col 46}{space 1}    1.71{col 55}{space 3}0.088{col 63}{space 4}-.0665713{col 76}{space 3} .9633571
{txt}{space 21} {c |}
{space 17}race {c |}
{space 15}Black  {c |}{col 23}{res}{space 2} .3776324{col 35}{space 2} .3852949{col 46}{space 1}    0.98{col 55}{space 3}0.327{col 63}{space 4}-.3775318{col 76}{space 3} 1.132797
{txt}{space 15}Other  {c |}{col 23}{res}{space 2} -.063592{col 35}{space 2} .4470377{col 46}{space 1}   -0.14{col 55}{space 3}0.887{col 63}{space 4}-.9397698{col 76}{space 3} .8125857
{txt}{space 21} {c |}
{space 14}age2004 {c |}{col 23}{res}{space 2} .0112966{col 35}{space 2} .0085512{col 46}{space 1}    1.32{col 55}{space 3}0.186{col 63}{space 4}-.0054634{col 76}{space 3} .0280567
{txt}{space 17}educ {c |}{col 23}{res}{space 2}-.2792262{col 35}{space 2} .1516062{col 46}{space 1}   -1.84{col 55}{space 3}0.066{col 63}{space 4}-.5763688{col 76}{space 3} .0179165
{txt}{space 15}income {c |}{col 23}{res}{space 2}-.0123761{col 35}{space 2} .0280366{col 46}{space 1}   -0.44{col 55}{space 3}0.659{col 63}{space 4}-.0673268{col 76}{space 3} .0425746
{txt}{space 21} {c |}
{space 14}marital {c |}
{space 13}Married  {c |}{col 23}{res}{space 2} .1277885{col 35}{space 2} .2873261{col 46}{space 1}    0.44{col 55}{space 3}0.656{col 63}{space 4}-.4353603{col 76}{space 3} .6909373
{txt}{space 12}evaluate1 {c |}{col 23}{res}{space 2}  .270319{col 35}{space 2} .3146074{col 46}{space 1}    0.86{col 55}{space 3}0.390{col 63}{space 4}-.3463002{col 76}{space 3} .8869382
{txt}{space 17}nfc1 {c |}{col 23}{res}{space 2}-.1595752{col 35}{space 2} .1987629{col 46}{space 1}   -0.80{col 55}{space 3}0.422{col 63}{space 4}-.5491432{col 76}{space 3} .2299929
{txt}{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                /cut1 {c |}{col 23}{res}{space 2}-4.195172{col 35}{space 2} 1.276916{col 63}{space 4}-6.697882{col 76}{space 3}-1.692462
{txt}                /cut2 {c |}{col 23}{res}{space 2}-1.546515{col 35}{space 2} 1.265693{col 63}{space 4}-4.027227{col 76}{space 3} .9341978
{txt}                /cut3 {c |}{col 23}{res}{space 2}-.2976091{col 35}{space 2} 1.282964{col 63}{space 4}-2.812173{col 76}{space 3} 2.216955
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est6{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-393.40137}  
Iteration 1:{space 3}log pseudolikelihood = {res: -378.2066}  
Iteration 2:{space 3}log pseudolikelihood = {res:-378.08643}  
Iteration 3:{space 3}log pseudolikelihood = {res:-378.08639}  
Iteration 4:{space 3}log pseudolikelihood = {res:-378.08639}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       394
{txt}{col 51}Wald chi2({res}14{txt}){col 67}= {res}     25.68
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0284
{txt}Log pseudolikelihood = {res}-378.08639{txt}{col 51}Pseudo R2{col 67}= {res}    0.0389

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1} pid_str_full{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 5}disc_gen {c |}{col 15}{res}{space 2}-.2529365{col 27}{space 2}  .138605{col 38}{space 1}   -1.82{col 47}{space 3}0.068{col 55}{space 4}-.5245972{col 68}{space 3} .0187243
{txt}{space 8}names {c |}{col 15}{res}{space 2} -.212445{col 27}{space 2} .2133637{col 38}{space 1}   -1.00{col 47}{space 3}0.319{col 55}{space 4}-.6306302{col 68}{space 3} .2057401
{txt}disc_interest {c |}{col 15}{res}{space 2} .1245963{col 27}{space 2} .1761308{col 38}{space 1}    0.71{col 47}{space 3}0.479{col 55}{space 4}-.2206138{col 68}{space 3} .4698064
{txt}{space 13} {c |}
{space 6}pid_204 {c |}
{space 4}Democrat  {c |}{col 15}{res}{space 2} .1633234{col 27}{space 2} .2551484{col 38}{space 1}    0.64{col 47}{space 3}0.522{col 55}{space 4}-.3367583{col 68}{space 3} .6634051
{txt}{space 5}follow04 {c |}{col 15}{res}{space 2} .0393392{col 27}{space 2} .1428455{col 38}{space 1}    0.28{col 47}{space 3}0.783{col 55}{space 4}-.2406329{col 68}{space 3} .3193113
{txt}{space 13} {c |}
{space 7}gender {c |}
{space 6}Female  {c |}{col 15}{res}{space 2} .4569771{col 27}{space 2} .2500118{col 38}{space 1}    1.83{col 47}{space 3}0.068{col 55}{space 4} -.033037{col 68}{space 3} .9469912
{txt}{space 13} {c |}
{space 9}race {c |}
{space 7}Black  {c |}{col 15}{res}{space 2} .4356646{col 27}{space 2} .3673719{col 38}{space 1}    1.19{col 47}{space 3}0.236{col 55}{space 4}-.2843711{col 68}{space 3}   1.1557
{txt}{space 7}Other  {c |}{col 15}{res}{space 2}-.1867636{col 27}{space 2} .4427725{col 38}{space 1}   -0.42{col 47}{space 3}0.673{col 55}{space 4}-1.054582{col 68}{space 3} .6810546
{txt}{space 13} {c |}
{space 6}age2004 {c |}{col 15}{res}{space 2} .0097986{col 27}{space 2} .0079241{col 38}{space 1}    1.24{col 47}{space 3}0.216{col 55}{space 4}-.0057324{col 68}{space 3} .0253296
{txt}{space 9}educ {c |}{col 15}{res}{space 2}-.2635835{col 27}{space 2} .1441528{col 38}{space 1}   -1.83{col 47}{space 3}0.067{col 55}{space 4}-.5461178{col 68}{space 3} .0189508
{txt}{space 7}income {c |}{col 15}{res}{space 2}-.0069123{col 27}{space 2} .0279185{col 38}{space 1}   -0.25{col 47}{space 3}0.804{col 55}{space 4}-.0616315{col 68}{space 3} .0478069
{txt}{space 13} {c |}
{space 6}marital {c |}
{space 5}Married  {c |}{col 15}{res}{space 2} .1288332{col 27}{space 2}  .281072{col 38}{space 1}    0.46{col 47}{space 3}0.647{col 55}{space 4}-.4220579{col 68}{space 3} .6797243
{txt}{space 4}evaluate1 {c |}{col 15}{res}{space 2} .3462347{col 27}{space 2} .3074414{col 38}{space 1}    1.13{col 47}{space 3}0.260{col 55}{space 4}-.2563393{col 68}{space 3} .9488087
{txt}{space 9}nfc1 {c |}{col 15}{res}{space 2} -.186667{col 27}{space 2} .1900518{col 38}{space 1}   -0.98{col 47}{space 3}0.326{col 55}{space 4}-.5591616{col 68}{space 3} .1858277
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
        /cut1 {c |}{col 15}{res}{space 2}-4.428007{col 27}{space 2} 1.246334{col 55}{space 4}-6.870776{col 68}{space 3}-1.985238
{txt}        /cut2 {c |}{col 15}{res}{space 2} -1.80983{col 27}{space 2} 1.216962{col 55}{space 4}-4.195031{col 68}{space 3} .5753717
{txt}        /cut3 {c |}{col 15}{res}{space 2}-.5856595{col 27}{space 2} 1.225575{col 55}{space 4}-2.987742{col 68}{space 3} 1.816423
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est7{txt} stored)

{com}. 
. esttab using 2006_ALTMEASURES_EXT.rtf, onecell nobaselevels replace label pr2 aic bic se star(+ 0.10 * 0.05 ** 0.01) ///
>         mtitles("Original Measure" "/(D+A)" "Weigh: Disc. Freq" "Weigh: Disc. Interest" ///
>                 "Weigh: Tie Strength" "Weigh: Gen Dis" "Gen. Disagree")  ///
>         title({c -(}\b Table XX.{c )-} "Alternative Measures of Disagreement - 2006 ANES") ///
>         rename(disagree_total Disagreement disagree_avg Disagreement disagree_total_freq Disagreement disagree_total_close Disagreement ///
>                                 disagree_total_int Disagreement disc_gen Disagreement disagree_total_weight Disagreement disc_gen Disagreement ///
>                                 1.partisan2004#c.disagree_total Partisan*Disagree ///                           
>                                 1.partisan2004#c.disagree_avg Partisan*Disagree ///
>                                 1.partisan2004#c.disagree_total_freq Partisan*Disagree ///
>                                 1.partisan2004#c.disagree_total_int Partisan*Disagree ///
>                                 1.partisan2004#c.disagree_total_close Partisan*Disagree ///
>                                 1.partisan2004#c.disagree_total_weight Partisan*Disagree ///
>                                 1.partisan2004#c.disc_gen Partisan*Disagree )
{res}{txt}(output written to {browse  `"2006_ALTMEASURES_EXT.rtf"'})

{com}. 
.                                 
. eststo clear
{txt}
{com}. 
. 
. /***Economic Evaluations***/
. eststo clear
{txt}
{com}. foreach var in disagree_total disagree_avg disagree_total_freq disagree_total_int ///
>                         disagree_total_close disagree_total_weight disc_gen {c -(}
{txt}  2{com}.         eststo: ologit retro i.partisan2004##c.`var' names disc_interest follow04 ///
>                 age educ income i.gender i.race i.employed i.marital nfc1 evaluate1 [pweight = V06P002] 
{txt}  3{com}.         {c )-}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-398.20582}  
Iteration 1:{space 3}log pseudolikelihood = {res:  -346.342}  
Iteration 2:{space 3}log pseudolikelihood = {res:-345.34455}  
Iteration 3:{space 3}log pseudolikelihood = {res:-345.34412}  
Iteration 4:{space 3}log pseudolikelihood = {res:-345.34412}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       391
{txt}{col 51}Wald chi2({res}18{txt}){col 67}= {res}     84.76
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-345.34412{txt}{col 51}Pseudo R2{col 67}= {res}    0.1327

{txt}{hline 30}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 31}{c |}{col 43}    Robust
{col 1}                        retro{col 31}{c |}      Coef.{col 43}   Std. Err.{col 55}      z{col 63}   P>|z|{col 71}     [95% Con{col 84}f. Interval]
{hline 30}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}partisan2004 {c |}
{space 17}In-Partisan  {c |}{col 31}{res}{space 2} 1.517085{col 43}{space 2} .3036955{col 54}{space 1}    5.00{col 63}{space 3}0.000{col 71}{space 4} .9218531{col 84}{space 3} 2.112318
{txt}{space 15}disagree_total {c |}{col 31}{res}{space 2} -.022964{col 43}{space 2} .1081594{col 54}{space 1}   -0.21{col 63}{space 3}0.832{col 71}{space 4}-.2349524{col 84}{space 3} .1890245
{txt}{space 29} {c |}
partisan2004#c.disagree_total {c |}
{space 17}In-Partisan  {c |}{col 31}{res}{space 2}-.2234281{col 43}{space 2} .1350508{col 54}{space 1}   -1.65{col 63}{space 3}0.098{col 71}{space 4}-.4881229{col 84}{space 3} .0412666
{txt}{space 29} {c |}
{space 24}names {c |}{col 31}{res}{space 2} .2201991{col 43}{space 2} .1844384{col 54}{space 1}    1.19{col 63}{space 3}0.233{col 71}{space 4}-.1412936{col 84}{space 3} .5816918
{txt}{space 16}disc_interest {c |}{col 31}{res}{space 2}-.1295055{col 43}{space 2} .1588351{col 54}{space 1}   -0.82{col 63}{space 3}0.415{col 71}{space 4}-.4408165{col 84}{space 3} .1818055
{txt}{space 21}follow04 {c |}{col 31}{res}{space 2} .2196008{col 43}{space 2} .1616811{col 54}{space 1}    1.36{col 63}{space 3}0.174{col 71}{space 4}-.0972883{col 84}{space 3} .5364899
{txt}{space 26}age {c |}{col 31}{res}{space 2}-.0064708{col 43}{space 2} .0109302{col 54}{space 1}   -0.59{col 63}{space 3}0.554{col 71}{space 4}-.0278936{col 84}{space 3} .0149519
{txt}{space 25}educ {c |}{col 31}{res}{space 2} .2101372{col 43}{space 2} .1570972{col 54}{space 1}    1.34{col 63}{space 3}0.181{col 71}{space 4}-.0977676{col 84}{space 3}  .518042
{txt}{space 23}income {c |}{col 31}{res}{space 2}-.0063398{col 43}{space 2} .0320112{col 54}{space 1}   -0.20{col 63}{space 3}0.843{col 71}{space 4}-.0690807{col 84}{space 3} .0564011
{txt}{space 29} {c |}
{space 23}gender {c |}
{space 22}Female  {c |}{col 31}{res}{space 2}-.6790265{col 43}{space 2} .2820748{col 54}{space 1}   -2.41{col 63}{space 3}0.016{col 71}{space 4}-1.231883{col 84}{space 3}  -.12617
{txt}{space 29} {c |}
{space 25}race {c |}
{space 23}Black  {c |}{col 31}{res}{space 2}-.3422644{col 43}{space 2} .3824246{col 54}{space 1}   -0.89{col 63}{space 3}0.371{col 71}{space 4}-1.091803{col 84}{space 3}  .407274
{txt}{space 23}Other  {c |}{col 31}{res}{space 2}-.2823911{col 43}{space 2} .4459434{col 54}{space 1}   -0.63{col 63}{space 3}0.527{col 71}{space 4}-1.156424{col 84}{space 3} .5916419
{txt}{space 29} {c |}
{space 21}employed {c |}
{space 18}Unemployed  {c |}{col 31}{res}{space 2} .8577024{col 43}{space 2} .6855795{col 54}{space 1}    1.25{col 63}{space 3}0.211{col 71}{space 4}-.4860088{col 84}{space 3} 2.201414
{txt}{space 21}Retired  {c |}{col 31}{res}{space 2} .0988804{col 43}{space 2} .4409436{col 54}{space 1}    0.22{col 63}{space 3}0.823{col 71}{space 4}-.7653533{col 84}{space 3} .9631141
{txt}{space 2}Disabled/Homemaker/Student  {c |}{col 31}{res}{space 2}-.3029962{col 43}{space 2} .3826053{col 54}{space 1}   -0.79{col 63}{space 3}0.428{col 71}{space 4}-1.052889{col 84}{space 3} .4468964
{txt}{space 29} {c |}
{space 22}marital {c |}
{space 21}Married  {c |}{col 31}{res}{space 2} .0078249{col 43}{space 2} .2770604{col 54}{space 1}    0.03{col 63}{space 3}0.977{col 71}{space 4}-.5352036{col 84}{space 3} .5508534
{txt}{space 25}nfc1 {c |}{col 31}{res}{space 2}-.1918412{col 43}{space 2}  .210103{col 54}{space 1}   -0.91{col 63}{space 3}0.361{col 71}{space 4}-.6036356{col 84}{space 3} .2199532
{txt}{space 20}evaluate1 {c |}{col 31}{res}{space 2}-.2872673{col 43}{space 2} .3160501{col 54}{space 1}   -0.91{col 63}{space 3}0.363{col 71}{space 4} -.906714{col 84}{space 3} .3321795
{txt}{hline 30}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                        /cut1 {c |}{col 31}{res}{space 2}-.3795137{col 43}{space 2} 1.006963{col 71}{space 4}-2.353126{col 84}{space 3} 1.594098
{txt}                        /cut2 {c |}{col 31}{res}{space 2} 1.715632{col 43}{space 2} .9803992{col 71}{space 4}-.2059155{col 84}{space 3} 3.637179
{txt}{hline 30}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-398.20582}  
Iteration 1:{space 3}log pseudolikelihood = {res:-347.51774}  
Iteration 2:{space 3}log pseudolikelihood = {res:-346.53937}  
Iteration 3:{space 3}log pseudolikelihood = {res:-346.53895}  
Iteration 4:{space 3}log pseudolikelihood = {res:-346.53895}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       391
{txt}{col 51}Wald chi2({res}18{txt}){col 67}= {res}     85.61
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-346.53895{txt}{col 51}Pseudo R2{col 67}= {res}    0.1297

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}                      retro{col 29}{c |}      Coef.{col 41}   Std. Err.{col 53}      z{col 61}   P>|z|{col 69}     [95% Con{col 82}f. Interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}partisan2004 {c |}
{space 15}In-Partisan  {c |}{col 29}{res}{space 2} 1.544335{col 41}{space 2} .3016052{col 52}{space 1}    5.12{col 61}{space 3}0.000{col 69}{space 4} .9531993{col 82}{space 3}  2.13547
{txt}{space 15}disagree_avg {c |}{col 29}{res}{space 2}-.0366355{col 41}{space 2} .2764239{col 52}{space 1}   -0.13{col 61}{space 3}0.895{col 69}{space 4}-.5784163{col 82}{space 3} .5051453
{txt}{space 27} {c |}
partisan2004#c.disagree_avg {c |}
{space 15}In-Partisan  {c |}{col 29}{res}{space 2}-.4713045{col 41}{space 2} .3596635{col 52}{space 1}   -1.31{col 61}{space 3}0.190{col 69}{space 4}-1.176232{col 82}{space 3} .2336229
{txt}{space 27} {c |}
{space 22}names {c |}{col 29}{res}{space 2} .2587362{col 41}{space 2} .1789137{col 52}{space 1}    1.45{col 61}{space 3}0.148{col 69}{space 4}-.0919281{col 82}{space 3} .6094006
{txt}{space 14}disc_interest {c |}{col 29}{res}{space 2}-.1158666{col 41}{space 2} .1576113{col 52}{space 1}   -0.74{col 61}{space 3}0.462{col 69}{space 4} -.424779{col 82}{space 3} .1930459
{txt}{space 19}follow04 {c |}{col 29}{res}{space 2} .2233524{col 41}{space 2} .1613779{col 52}{space 1}    1.38{col 61}{space 3}0.166{col 69}{space 4}-.0929425{col 82}{space 3} .5396472
{txt}{space 24}age {c |}{col 29}{res}{space 2}-.0069979{col 41}{space 2} .0109081{col 52}{space 1}   -0.64{col 61}{space 3}0.521{col 69}{space 4}-.0283773{col 82}{space 3} .0143815
{txt}{space 23}educ {c |}{col 29}{res}{space 2}  .204232{col 41}{space 2} .1571197{col 52}{space 1}    1.30{col 61}{space 3}0.194{col 69}{space 4} -.103717{col 82}{space 3}  .512181
{txt}{space 21}income {c |}{col 29}{res}{space 2}-.0038983{col 41}{space 2} .0319192{col 52}{space 1}   -0.12{col 61}{space 3}0.903{col 69}{space 4}-.0664589{col 82}{space 3} .0586622
{txt}{space 27} {c |}
{space 21}gender {c |}
{space 20}Female  {c |}{col 29}{res}{space 2}-.6468713{col 41}{space 2} .2803884{col 52}{space 1}   -2.31{col 61}{space 3}0.021{col 69}{space 4}-1.196422{col 82}{space 3}-.0973202
{txt}{space 27} {c |}
{space 23}race {c |}
{space 21}Black  {c |}{col 29}{res}{space 2}-.3446745{col 41}{space 2} .3805946{col 52}{space 1}   -0.91{col 61}{space 3}0.365{col 69}{space 4}-1.090626{col 82}{space 3} .4012772
{txt}{space 21}Other  {c |}{col 29}{res}{space 2}-.2938719{col 41}{space 2} .4513901{col 52}{space 1}   -0.65{col 61}{space 3}0.515{col 69}{space 4} -1.17858{col 82}{space 3} .5908365
{txt}{space 27} {c |}
{space 19}employed {c |}
{space 16}Unemployed  {c |}{col 29}{res}{space 2} .8542823{col 41}{space 2} .7181711{col 52}{space 1}    1.19{col 61}{space 3}0.234{col 69}{space 4}-.5533072{col 82}{space 3} 2.261872
{txt}{space 19}Retired  {c |}{col 29}{res}{space 2} .1230866{col 41}{space 2}  .439732{col 52}{space 1}    0.28{col 61}{space 3}0.780{col 69}{space 4}-.7387723{col 82}{space 3} .9849455
{txt}Disabled/Homemaker/Student  {c |}{col 29}{res}{space 2}-.2614606{col 41}{space 2} .3757613{col 52}{space 1}   -0.70{col 61}{space 3}0.487{col 69}{space 4}-.9979392{col 82}{space 3}  .475018
{txt}{space 27} {c |}
{space 20}marital {c |}
{space 19}Married  {c |}{col 29}{res}{space 2}-.0034716{col 41}{space 2} .2783956{col 52}{space 1}   -0.01{col 61}{space 3}0.990{col 69}{space 4}-.5491169{col 82}{space 3} .5421738
{txt}{space 23}nfc1 {c |}{col 29}{res}{space 2}-.1969199{col 41}{space 2} .2087443{col 52}{space 1}   -0.94{col 61}{space 3}0.345{col 69}{space 4}-.6060511{col 82}{space 3} .2122114
{txt}{space 18}evaluate1 {c |}{col 29}{res}{space 2}-.2558504{col 41}{space 2} .3151703{col 52}{space 1}   -0.81{col 61}{space 3}0.417{col 69}{space 4}-.8735729{col 82}{space 3} .3618721
{txt}{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                      /cut1 {c |}{col 29}{res}{space 2}-.1817583{col 41}{space 2} 1.010472{col 69}{space 4}-2.162247{col 82}{space 3}  1.79873
{txt}                      /cut2 {c |}{col 29}{res}{space 2} 1.906081{col 41}{space 2} .9863725{col 69}{space 4}-.0271736{col 82}{space 3} 3.839335
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-398.20582}  
Iteration 1:{space 3}log pseudolikelihood = {res:-347.10975}  
Iteration 2:{space 3}log pseudolikelihood = {res:-346.12782}  
Iteration 3:{space 3}log pseudolikelihood = {res:-346.12741}  
Iteration 4:{space 3}log pseudolikelihood = {res:-346.12741}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       391
{txt}{col 51}Wald chi2({res}18{txt}){col 67}= {res}     83.13
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-346.12741{txt}{col 51}Pseudo R2{col 67}= {res}    0.1308

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}                             retro{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      z{col 68}   P>|z|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 22}partisan2004 {c |}
{space 22}In-Partisan  {c |}{col 36}{res}{space 2} 1.538379{col 48}{space 2} .2982484{col 59}{space 1}    5.16{col 68}{space 3}0.000{col 76}{space 4} .9538226{col 89}{space 3} 2.122935
{txt}{space 15}disagree_total_freq {c |}{col 36}{res}{space 2}-.0040223{col 48}{space 2} .0297022{col 59}{space 1}   -0.14{col 68}{space 3}0.892{col 76}{space 4}-.0622374{col 89}{space 3} .0541929
{txt}{space 34} {c |}
partisan2004#c.disagree_total_freq {c |}
{space 22}In-Partisan  {c |}{col 36}{res}{space 2}-.0576809{col 48}{space 2}  .038372{col 59}{space 1}   -1.50{col 68}{space 3}0.133{col 76}{space 4}-.1328887{col 89}{space 3} .0175269
{txt}{space 34} {c |}
{space 29}names {c |}{col 36}{res}{space 2} .2310758{col 48}{space 2} .1837473{col 59}{space 1}    1.26{col 68}{space 3}0.209{col 76}{space 4}-.1290622{col 89}{space 3} .5912138
{txt}{space 21}disc_interest {c |}{col 36}{res}{space 2}-.1211176{col 48}{space 2} .1595579{col 59}{space 1}   -0.76{col 68}{space 3}0.448{col 76}{space 4}-.4338454{col 89}{space 3} .1916102
{txt}{space 26}follow04 {c |}{col 36}{res}{space 2} .2128685{col 48}{space 2}  .162559{col 59}{space 1}    1.31{col 68}{space 3}0.190{col 76}{space 4}-.1057413{col 89}{space 3} .5314783
{txt}{space 31}age {c |}{col 36}{res}{space 2}-.0070293{col 48}{space 2} .0110499{col 59}{space 1}   -0.64{col 68}{space 3}0.525{col 76}{space 4}-.0286867{col 89}{space 3} .0146282
{txt}{space 30}educ {c |}{col 36}{res}{space 2} .2076766{col 48}{space 2} .1573691{col 59}{space 1}    1.32{col 68}{space 3}0.187{col 76}{space 4}-.1007612{col 89}{space 3} .5161144
{txt}{space 28}income {c |}{col 36}{res}{space 2}-.0053289{col 48}{space 2}  .031996{col 59}{space 1}   -0.17{col 68}{space 3}0.868{col 76}{space 4}  -.06804{col 89}{space 3} .0573822
{txt}{space 34} {c |}
{space 28}gender {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.6933818{col 48}{space 2} .2845747{col 59}{space 1}   -2.44{col 68}{space 3}0.015{col 76}{space 4}-1.251138{col 89}{space 3}-.1356257
{txt}{space 34} {c |}
{space 30}race {c |}
{space 28}Black  {c |}{col 36}{res}{space 2}-.3386376{col 48}{space 2} .3753268{col 59}{space 1}   -0.90{col 68}{space 3}0.367{col 76}{space 4}-1.074265{col 89}{space 3} .3969895
{txt}{space 28}Other  {c |}{col 36}{res}{space 2} -.305084{col 48}{space 2} .4488836{col 59}{space 1}   -0.68{col 68}{space 3}0.497{col 76}{space 4} -1.18488{col 89}{space 3} .5747116
{txt}{space 34} {c |}
{space 26}employed {c |}
{space 23}Unemployed  {c |}{col 36}{res}{space 2} .8318615{col 48}{space 2} .6196233{col 59}{space 1}    1.34{col 68}{space 3}0.179{col 76}{space 4}-.3825779{col 89}{space 3} 2.046301
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2} .1334383{col 48}{space 2} .4403451{col 59}{space 1}    0.30{col 68}{space 3}0.762{col 76}{space 4}-.7296222{col 89}{space 3} .9964988
{txt}{space 7}Disabled/Homemaker/Student  {c |}{col 36}{res}{space 2}-.2771055{col 48}{space 2} .3840967{col 59}{space 1}   -0.72{col 68}{space 3}0.471{col 76}{space 4}-1.029921{col 89}{space 3} .4757101
{txt}{space 34} {c |}
{space 27}marital {c |}
{space 26}Married  {c |}{col 36}{res}{space 2} .0172973{col 48}{space 2} .2784184{col 59}{space 1}    0.06{col 68}{space 3}0.950{col 76}{space 4}-.5283929{col 89}{space 3} .5629874
{txt}{space 30}nfc1 {c |}{col 36}{res}{space 2}-.1986494{col 48}{space 2} .2098268{col 59}{space 1}   -0.95{col 68}{space 3}0.344{col 76}{space 4}-.6099023{col 89}{space 3} .2126035
{txt}{space 25}evaluate1 {c |}{col 36}{res}{space 2}-.2616534{col 48}{space 2} .3165511{col 59}{space 1}   -0.83{col 68}{space 3}0.408{col 76}{space 4}-.8820822{col 89}{space 3} .3587753
{txt}{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                             /cut1 {c |}{col 36}{res}{space 2}-.3432608{col 48}{space 2} 1.018452{col 76}{space 4} -2.33939{col 89}{space 3} 1.652868
{txt}                             /cut2 {c |}{col 36}{res}{space 2} 1.746947{col 48}{space 2} .9927988{col 76}{space 4}-.1989034{col 89}{space 3} 3.692797
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-398.20582}  
Iteration 1:{space 3}log pseudolikelihood = {res:-346.32941}  
Iteration 2:{space 3}log pseudolikelihood = {res:-345.33541}  
Iteration 3:{space 3}log pseudolikelihood = {res:-345.33505}  
Iteration 4:{space 3}log pseudolikelihood = {res:-345.33505}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       391
{txt}{col 51}Wald chi2({res}18{txt}){col 67}= {res}     82.46
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-345.33505{txt}{col 51}Pseudo R2{col 67}= {res}    0.1328

{txt}{hline 34}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 35}{c |}{col 47}    Robust
{col 1}                            retro{col 35}{c |}      Coef.{col 47}   Std. Err.{col 59}      z{col 67}   P>|z|{col 75}     [95% Con{col 88}f. Interval]
{hline 34}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}partisan2004 {c |}
{space 21}In-Partisan  {c |}{col 35}{res}{space 2} 1.411425{col 47}{space 2}  .300274{col 58}{space 1}    4.70{col 67}{space 3}0.000{col 75}{space 4} .8228989{col 88}{space 3} 1.999952
{txt}{space 15}disagree_total_int {c |}{col 35}{res}{space 2} .0112264{col 47}{space 2} .0278548{col 58}{space 1}    0.40{col 67}{space 3}0.687{col 75}{space 4} -.043368{col 88}{space 3} .0658209
{txt}{space 33} {c |}
partisan2004#c.disagree_total_int {c |}
{space 21}In-Partisan  {c |}{col 35}{res}{space 2}-.0782217{col 47}{space 2} .0362495{col 58}{space 1}   -2.16{col 67}{space 3}0.031{col 75}{space 4}-.1492694{col 88}{space 3}-.0071741
{txt}{space 33} {c |}
{space 28}names {c |}{col 35}{res}{space 2} .2335122{col 47}{space 2} .1820028{col 58}{space 1}    1.28{col 67}{space 3}0.199{col 75}{space 4}-.1232068{col 88}{space 3} .5902312
{txt}{space 20}disc_interest {c |}{col 35}{res}{space 2}-.1313934{col 47}{space 2} .1582556{col 58}{space 1}   -0.83{col 67}{space 3}0.406{col 75}{space 4}-.4415686{col 88}{space 3} .1787818
{txt}{space 25}follow04 {c |}{col 35}{res}{space 2} .2170765{col 47}{space 2}  .163546{col 58}{space 1}    1.33{col 67}{space 3}0.184{col 75}{space 4}-.1034678{col 88}{space 3} .5376208
{txt}{space 30}age {c |}{col 35}{res}{space 2}-.0064266{col 47}{space 2} .0108916{col 58}{space 1}   -0.59{col 67}{space 3}0.555{col 75}{space 4}-.0277738{col 88}{space 3} .0149206
{txt}{space 29}educ {c |}{col 35}{res}{space 2} .2140394{col 47}{space 2} .1569376{col 58}{space 1}    1.36{col 67}{space 3}0.173{col 75}{space 4}-.0935527{col 88}{space 3} .5216315
{txt}{space 27}income {c |}{col 35}{res}{space 2}-.0059932{col 47}{space 2} .0321455{col 58}{space 1}   -0.19{col 67}{space 3}0.852{col 75}{space 4}-.0689971{col 88}{space 3} .0570108
{txt}{space 33} {c |}
{space 27}gender {c |}
{space 26}Female  {c |}{col 35}{res}{space 2}-.6820974{col 47}{space 2} .2799813{col 58}{space 1}   -2.44{col 67}{space 3}0.015{col 75}{space 4}-1.230851{col 88}{space 3}-.1333442
{txt}{space 33} {c |}
{space 29}race {c |}
{space 27}Black  {c |}{col 35}{res}{space 2}-.3016639{col 47}{space 2} .3814146{col 58}{space 1}   -0.79{col 67}{space 3}0.429{col 75}{space 4}-1.049223{col 88}{space 3} .4458949
{txt}{space 27}Other  {c |}{col 35}{res}{space 2}-.2759411{col 47}{space 2} .4547813{col 58}{space 1}   -0.61{col 67}{space 3}0.544{col 75}{space 4}-1.167296{col 88}{space 3} .6154139
{txt}{space 33} {c |}
{space 25}employed {c |}
{space 22}Unemployed  {c |}{col 35}{res}{space 2}  .743704{col 47}{space 2} .6108583{col 58}{space 1}    1.22{col 67}{space 3}0.223{col 75}{space 4}-.4535563{col 88}{space 3} 1.940964
{txt}{space 25}Retired  {c |}{col 35}{res}{space 2} .0965338{col 47}{space 2} .4395755{col 58}{space 1}    0.22{col 67}{space 3}0.826{col 75}{space 4}-.7650184{col 88}{space 3}  .958086
{txt}{space 6}Disabled/Homemaker/Student  {c |}{col 35}{res}{space 2}-.2643074{col 47}{space 2} .3837526{col 58}{space 1}   -0.69{col 67}{space 3}0.491{col 75}{space 4}-1.016449{col 88}{space 3} .4878339
{txt}{space 33} {c |}
{space 26}marital {c |}
{space 25}Married  {c |}{col 35}{res}{space 2} .0194409{col 47}{space 2} .2767077{col 58}{space 1}    0.07{col 67}{space 3}0.944{col 75}{space 4}-.5228961{col 88}{space 3}  .561778
{txt}{space 29}nfc1 {c |}{col 35}{res}{space 2}-.2064529{col 47}{space 2} .2097099{col 58}{space 1}   -0.98{col 67}{space 3}0.325{col 75}{space 4}-.6174767{col 88}{space 3}  .204571
{txt}{space 24}evaluate1 {c |}{col 35}{res}{space 2}-.2891645{col 47}{space 2} .3163928{col 58}{space 1}   -0.91{col 67}{space 3}0.361{col 75}{space 4} -.909283{col 88}{space 3}  .330954
{txt}{hline 34}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                            /cut1 {c |}{col 35}{res}{space 2}-.4337224{col 47}{space 2} 1.011822{col 75}{space 4}-2.416857{col 88}{space 3} 1.549412
{txt}                            /cut2 {c |}{col 35}{res}{space 2} 1.662312{col 47}{space 2} .9864778{col 75}{space 4}-.2711487{col 88}{space 3} 3.595773
{txt}{hline 34}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-398.20582}  
Iteration 1:{space 3}log pseudolikelihood = {res:-346.84003}  
Iteration 2:{space 3}log pseudolikelihood = {res:-345.84539}  
Iteration 3:{space 3}log pseudolikelihood = {res:-345.84495}  
Iteration 4:{space 3}log pseudolikelihood = {res:-345.84495}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       391
{txt}{col 51}Wald chi2({res}18{txt}){col 67}= {res}     86.52
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-345.84495{txt}{col 51}Pseudo R2{col 67}= {res}    0.1315

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}                      retro{col 29}{c |}      Coef.{col 41}   Std. Err.{col 53}      z{col 61}   P>|z|{col 69}     [95% Con{col 82}f. Interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}partisan2004 {c |}
{space 15}In-Partisan  {c |}{col 29}{res}{space 2} 1.629487{col 41}{space 2} .3057852{col 52}{space 1}    5.33{col 61}{space 3}0.000{col 69}{space 4} 1.030159{col 82}{space 3} 2.228815
{txt}{space 7}disagree_total_close {c |}{col 29}{res}{space 2}-.0190743{col 41}{space 2} .0241397{col 52}{space 1}   -0.79{col 61}{space 3}0.429{col 69}{space 4}-.0663872{col 82}{space 3} .0282386
{txt}{space 27} {c |}
{space 15}partisan2004#{c |}
{space 5}c.disagree_total_close {c |}
{space 15}In-Partisan  {c |}{col 29}{res}{space 2}-.0331333{col 41}{space 2} .0318312{col 52}{space 1}   -1.04{col 61}{space 3}0.298{col 69}{space 4}-.0955212{col 82}{space 3} .0292546
{txt}{space 27} {c |}
{space 22}names {c |}{col 29}{res}{space 2} .2096716{col 41}{space 2}  .186577{col 52}{space 1}    1.12{col 61}{space 3}0.261{col 69}{space 4}-.1560126{col 82}{space 3} .5753558
{txt}{space 14}disc_interest {c |}{col 29}{res}{space 2}-.1276754{col 41}{space 2} .1594076{col 52}{space 1}   -0.80{col 61}{space 3}0.423{col 69}{space 4}-.4401086{col 82}{space 3} .1847578
{txt}{space 19}follow04 {c |}{col 29}{res}{space 2}  .211653{col 41}{space 2} .1615706{col 52}{space 1}    1.31{col 61}{space 3}0.190{col 69}{space 4}-.1050196{col 82}{space 3} .5283256
{txt}{space 24}age {c |}{col 29}{res}{space 2}-.0069098{col 41}{space 2} .0109261{col 52}{space 1}   -0.63{col 61}{space 3}0.527{col 69}{space 4}-.0283246{col 82}{space 3}  .014505
{txt}{space 23}educ {c |}{col 29}{res}{space 2} .2132455{col 41}{space 2} .1567704{col 52}{space 1}    1.36{col 61}{space 3}0.174{col 69}{space 4}-.0940188{col 82}{space 3} .5205098
{txt}{space 21}income {c |}{col 29}{res}{space 2}-.0051175{col 41}{space 2} .0319589{col 52}{space 1}   -0.16{col 61}{space 3}0.873{col 69}{space 4}-.0677558{col 82}{space 3} .0575209
{txt}{space 27} {c |}
{space 21}gender {c |}
{space 20}Female  {c |}{col 29}{res}{space 2}-.6831813{col 41}{space 2}  .285069{col 52}{space 1}   -2.40{col 61}{space 3}0.017{col 69}{space 4}-1.241906{col 82}{space 3}-.1244564
{txt}{space 27} {c |}
{space 23}race {c |}
{space 21}Black  {c |}{col 29}{res}{space 2}-.3627525{col 41}{space 2}  .375646{col 52}{space 1}   -0.97{col 61}{space 3}0.334{col 69}{space 4}-1.099005{col 82}{space 3} .3735002
{txt}{space 21}Other  {c |}{col 29}{res}{space 2}-.2766881{col 41}{space 2} .4415195{col 52}{space 1}   -0.63{col 61}{space 3}0.531{col 69}{space 4} -1.14205{col 82}{space 3} .5886742
{txt}{space 27} {c |}
{space 19}employed {c |}
{space 16}Unemployed  {c |}{col 29}{res}{space 2} .9497346{col 41}{space 2} .6698652{col 52}{space 1}    1.42{col 61}{space 3}0.156{col 69}{space 4}-.3631769{col 82}{space 3} 2.262646
{txt}{space 19}Retired  {c |}{col 29}{res}{space 2} .1126586{col 41}{space 2}  .444439{col 52}{space 1}    0.25{col 61}{space 3}0.800{col 69}{space 4}-.7584258{col 82}{space 3}  .983743
{txt}Disabled/Homemaker/Student  {c |}{col 29}{res}{space 2}-.3130324{col 41}{space 2}  .383264{col 52}{space 1}   -0.82{col 61}{space 3}0.414{col 69}{space 4}-1.064216{col 82}{space 3} .4381512
{txt}{space 27} {c |}
{space 20}marital {c |}
{space 19}Married  {c |}{col 29}{res}{space 2}-.0006199{col 41}{space 2} .2768909{col 52}{space 1}   -0.00{col 61}{space 3}0.998{col 69}{space 4}-.5433161{col 82}{space 3} .5420763
{txt}{space 23}nfc1 {c |}{col 29}{res}{space 2}-.1940182{col 41}{space 2} .2095205{col 52}{space 1}   -0.93{col 61}{space 3}0.354{col 69}{space 4}-.6046709{col 82}{space 3} .2166345
{txt}{space 18}evaluate1 {c |}{col 29}{res}{space 2}-.2764089{col 41}{space 2} .3185994{col 52}{space 1}   -0.87{col 61}{space 3}0.386{col 69}{space 4}-.9008521{col 82}{space 3} .3480344
{txt}{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                      /cut1 {c |}{col 29}{res}{space 2}-.3239595{col 41}{space 2} 1.010547{col 69}{space 4}-2.304594{col 82}{space 3} 1.656676
{txt}                      /cut2 {c |}{col 29}{res}{space 2} 1.768833{col 41}{space 2}  .982579{col 69}{space 4}-.1569867{col 82}{space 3} 3.694652
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est5{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-398.20582}  
Iteration 1:{space 3}log pseudolikelihood = {res:-346.31358}  
Iteration 2:{space 3}log pseudolikelihood = {res: -345.3204}  
Iteration 3:{space 3}log pseudolikelihood = {res:-345.31997}  
Iteration 4:{space 3}log pseudolikelihood = {res:-345.31997}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       391
{txt}{col 51}Wald chi2({res}18{txt}){col 67}= {res}     82.49
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-345.31997{txt}{col 51}Pseudo R2{col 67}= {res}    0.1328

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}                      retro{col 29}{c |}      Coef.{col 41}   Std. Err.{col 53}      z{col 61}   P>|z|{col 69}     [95% Con{col 82}f. Interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}partisan2004 {c |}
{space 15}In-Partisan  {c |}{col 29}{res}{space 2} 1.436997{col 41}{space 2} .3177257{col 52}{space 1}    4.52{col 61}{space 3}0.000{col 69}{space 4} .8142662{col 82}{space 3} 2.059728
{txt}{space 6}disagree_total_weight {c |}{col 29}{res}{space 2}-.0019548{col 41}{space 2} .0288546{col 52}{space 1}   -0.07{col 61}{space 3}0.946{col 69}{space 4}-.0585089{col 82}{space 3} .0545992
{txt}{space 27} {c |}
{space 15}partisan2004#{c |}
{space 4}c.disagree_total_weight {c |}
{space 15}In-Partisan  {c |}{col 29}{res}{space 2}-.0640608{col 41}{space 2} .0364342{col 52}{space 1}   -1.76{col 61}{space 3}0.079{col 69}{space 4}-.1354705{col 82}{space 3}  .007349
{txt}{space 27} {c |}
{space 22}names {c |}{col 29}{res}{space 2}   .20926{col 41}{space 2} .1838687{col 52}{space 1}    1.14{col 61}{space 3}0.255{col 69}{space 4} -.151116{col 82}{space 3}  .569636
{txt}{space 14}disc_interest {c |}{col 29}{res}{space 2}-.1321438{col 41}{space 2}  .158475{col 52}{space 1}   -0.83{col 61}{space 3}0.404{col 69}{space 4}-.4427492{col 82}{space 3} .1784616
{txt}{space 19}follow04 {c |}{col 29}{res}{space 2} .2077092{col 41}{space 2} .1634488{col 52}{space 1}    1.27{col 61}{space 3}0.204{col 69}{space 4}-.1126446{col 82}{space 3}  .528063
{txt}{space 24}age {c |}{col 29}{res}{space 2}-.0059782{col 41}{space 2} .0109268{col 52}{space 1}   -0.55{col 61}{space 3}0.584{col 69}{space 4}-.0273943{col 82}{space 3} .0154379
{txt}{space 23}educ {c |}{col 29}{res}{space 2} .2217606{col 41}{space 2} .1576776{col 52}{space 1}    1.41{col 61}{space 3}0.160{col 69}{space 4}-.0872818{col 82}{space 3}  .530803
{txt}{space 21}income {c |}{col 29}{res}{space 2}-.0076732{col 41}{space 2}  .031966{col 52}{space 1}   -0.24{col 61}{space 3}0.810{col 69}{space 4}-.0703254{col 82}{space 3} .0549789
{txt}{space 27} {c |}
{space 21}gender {c |}
{space 20}Female  {c |}{col 29}{res}{space 2}-.6919456{col 41}{space 2} .2816451{col 52}{space 1}   -2.46{col 61}{space 3}0.014{col 69}{space 4} -1.24396{col 82}{space 3}-.1399314
{txt}{space 27} {c |}
{space 23}race {c |}
{space 21}Black  {c |}{col 29}{res}{space 2} -.328772{col 41}{space 2} .3799027{col 52}{space 1}   -0.87{col 61}{space 3}0.387{col 69}{space 4}-1.073368{col 82}{space 3} .4158235
{txt}{space 21}Other  {c |}{col 29}{res}{space 2}-.2871958{col 41}{space 2} .4390034{col 52}{space 1}   -0.65{col 61}{space 3}0.513{col 69}{space 4}-1.147627{col 82}{space 3}  .573235
{txt}{space 27} {c |}
{space 19}employed {c |}
{space 16}Unemployed  {c |}{col 29}{res}{space 2} .8175174{col 41}{space 2}   .65898{col 52}{space 1}    1.24{col 61}{space 3}0.215{col 69}{space 4}-.4740597{col 82}{space 3} 2.109094
{txt}{space 19}Retired  {c |}{col 29}{res}{space 2} .1064302{col 41}{space 2} .4390998{col 52}{space 1}    0.24{col 61}{space 3}0.808{col 69}{space 4}-.7541895{col 82}{space 3} .9670499
{txt}Disabled/Homemaker/Student  {c |}{col 29}{res}{space 2}-.2893434{col 41}{space 2} .3827853{col 52}{space 1}   -0.76{col 61}{space 3}0.450{col 69}{space 4}-1.039589{col 82}{space 3} .4609019
{txt}{space 27} {c |}
{space 20}marital {c |}
{space 19}Married  {c |}{col 29}{res}{space 2} .0027377{col 41}{space 2} .2788736{col 52}{space 1}    0.01{col 61}{space 3}0.992{col 69}{space 4}-.5438444{col 82}{space 3} .5493199
{txt}{space 23}nfc1 {c |}{col 29}{res}{space 2}-.1988273{col 41}{space 2}  .210888{col 52}{space 1}   -0.94{col 61}{space 3}0.346{col 69}{space 4}-.6121602{col 82}{space 3} .2145055
{txt}{space 18}evaluate1 {c |}{col 29}{res}{space 2}-.2842135{col 41}{space 2} .3170425{col 52}{space 1}   -0.90{col 61}{space 3}0.370{col 69}{space 4}-.9056053{col 82}{space 3} .3371784
{txt}{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                      /cut1 {c |}{col 29}{res}{space 2}-.4488127{col 41}{space 2} 1.005841{col 69}{space 4}-2.420224{col 82}{space 3} 1.522599
{txt}                      /cut2 {c |}{col 29}{res}{space 2} 1.645976{col 41}{space 2} .9796574{col 69}{space 4}-.2741168{col 82}{space 3} 3.566069
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est6{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-400.09486}  
Iteration 1:{space 3}log pseudolikelihood = {res:-350.11354}  
Iteration 2:{space 3}log pseudolikelihood = {res:-349.15551}  
Iteration 3:{space 3}log pseudolikelihood = {res:-349.15506}  
Iteration 4:{space 3}log pseudolikelihood = {res:-349.15506}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       394
{txt}{col 51}Wald chi2({res}18{txt}){col 67}= {res}     81.49
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-349.15506{txt}{col 51}Pseudo R2{col 67}= {res}    0.1273

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}                      retro{col 29}{c |}      Coef.{col 41}   Std. Err.{col 53}      z{col 61}   P>|z|{col 69}     [95% Con{col 82}f. Interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}partisan2004 {c |}
{space 15}In-Partisan  {c |}{col 29}{res}{space 2} 2.602332{col 41}{space 2} .7701719{col 52}{space 1}    3.38{col 61}{space 3}0.001{col 69}{space 4} 1.092823{col 82}{space 3} 4.111842
{txt}{space 19}disc_gen {c |}{col 29}{res}{space 2} .0508414{col 41}{space 2}  .206259{col 52}{space 1}    0.25{col 61}{space 3}0.805{col 69}{space 4}-.3534187{col 82}{space 3} .4551015
{txt}{space 27} {c |}
{space 4}partisan2004#c.disc_gen {c |}
{space 15}In-Partisan  {c |}{col 29}{res}{space 2}-.3782007{col 41}{space 2} .2965346{col 52}{space 1}   -1.28{col 61}{space 3}0.202{col 69}{space 4}-.9593978{col 82}{space 3} .2029965
{txt}{space 27} {c |}
{space 22}names {c |}{col 29}{res}{space 2}  .211186{col 41}{space 2} .1742255{col 52}{space 1}    1.21{col 61}{space 3}0.225{col 69}{space 4}-.1302898{col 82}{space 3} .5526617
{txt}{space 14}disc_interest {c |}{col 29}{res}{space 2} -.096616{col 41}{space 2}  .159756{col 52}{space 1}   -0.60{col 61}{space 3}0.545{col 69}{space 4} -.409732{col 82}{space 3}    .2165
{txt}{space 19}follow04 {c |}{col 29}{res}{space 2} .2108185{col 41}{space 2} .1654663{col 52}{space 1}    1.27{col 61}{space 3}0.203{col 69}{space 4}-.1134894{col 82}{space 3} .5351264
{txt}{space 24}age {c |}{col 29}{res}{space 2}-.0074866{col 41}{space 2} .0107224{col 52}{space 1}   -0.70{col 61}{space 3}0.485{col 69}{space 4}-.0285021{col 82}{space 3} .0135288
{txt}{space 23}educ {c |}{col 29}{res}{space 2} .2261639{col 41}{space 2} .1588336{col 52}{space 1}    1.42{col 61}{space 3}0.154{col 69}{space 4}-.0851443{col 82}{space 3}  .537472
{txt}{space 21}income {c |}{col 29}{res}{space 2}-.0050776{col 41}{space 2} .0318318{col 52}{space 1}   -0.16{col 61}{space 3}0.873{col 69}{space 4}-.0674667{col 82}{space 3} .0573116
{txt}{space 27} {c |}
{space 21}gender {c |}
{space 20}Female  {c |}{col 29}{res}{space 2}-.6366705{col 41}{space 2} .2775601{col 52}{space 1}   -2.29{col 61}{space 3}0.022{col 69}{space 4}-1.180678{col 82}{space 3}-.0926627
{txt}{space 27} {c |}
{space 23}race {c |}
{space 21}Black  {c |}{col 29}{res}{space 2}-.3805786{col 41}{space 2} .3737303{col 52}{space 1}   -1.02{col 61}{space 3}0.309{col 69}{space 4}-1.113077{col 82}{space 3} .3519195
{txt}{space 21}Other  {c |}{col 29}{res}{space 2}-.3261632{col 41}{space 2} .4330398{col 52}{space 1}   -0.75{col 61}{space 3}0.451{col 69}{space 4}-1.174906{col 82}{space 3} .5225793
{txt}{space 27} {c |}
{space 19}employed {c |}
{space 16}Unemployed  {c |}{col 29}{res}{space 2} .7415646{col 41}{space 2}  .579819{col 52}{space 1}    1.28{col 61}{space 3}0.201{col 69}{space 4}-.3948598{col 82}{space 3} 1.877989
{txt}{space 19}Retired  {c |}{col 29}{res}{space 2} .1397362{col 41}{space 2} .4376082{col 52}{space 1}    0.32{col 61}{space 3}0.749{col 69}{space 4}-.7179601{col 82}{space 3} .9974324
{txt}Disabled/Homemaker/Student  {c |}{col 29}{res}{space 2} -.165612{col 41}{space 2} .3696728{col 52}{space 1}   -0.45{col 61}{space 3}0.654{col 69}{space 4}-.8901574{col 82}{space 3} .5589335
{txt}{space 27} {c |}
{space 20}marital {c |}
{space 19}Married  {c |}{col 29}{res}{space 2}-.0505427{col 41}{space 2} .2794139{col 52}{space 1}   -0.18{col 61}{space 3}0.856{col 69}{space 4}-.5981839{col 82}{space 3} .4970985
{txt}{space 23}nfc1 {c |}{col 29}{res}{space 2}-.2363251{col 41}{space 2} .2059602{col 52}{space 1}   -1.15{col 61}{space 3}0.251{col 69}{space 4}-.6399997{col 82}{space 3} .1673494
{txt}{space 18}evaluate1 {c |}{col 29}{res}{space 2}-.1930466{col 41}{space 2} .3162452{col 52}{space 1}   -0.61{col 61}{space 3}0.542{col 69}{space 4}-.8128757{col 82}{space 3} .4267825
{txt}{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                      /cut1 {c |}{col 29}{res}{space 2}-.1509068{col 41}{space 2} 1.186786{col 69}{space 4}-2.476966{col 82}{space 3} 2.175152
{txt}                      /cut2 {c |}{col 29}{res}{space 2} 1.937662{col 41}{space 2} 1.166223{col 69}{space 4}-.3480927{col 82}{space 3} 4.223418
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est7{txt} stored)

{com}.         
.         
. esttab using 2006_ALTMEASURES_ECON.rtf, onecell nobaselevels replace  pr2 aic bic se star(+ 0.10 * 0.05 ** 0.01) ///
>         mtitles("Original Measure" "/(D+A)" "Weigh: Disc. Freq" "Weigh: Disc. Interest" ///
>                 "Weigh: Tie Strength" "Weigh: Gen Dis" "Gen. Disagree")  ///
>         title({c -(}\b Table XX.{c )-} "Alternative Measures of Disagreement - 2006 ANES") ///
>         rename(disagree_total Disagreement disagree_avg Disagreement disagree_total_freq Disagreement ///
>                                 disagree_total_int Disagreement disc_gen Disagreement disagree_total_weight Disagreement disc_gen Disagreement) 
{res}{txt}(output written to {browse  `"2006_ALTMEASURES_ECON.rtf"'})

{com}. 
. eststo clear    
{txt}
{com}. 
. 
. /****************************************
> *****************************************
>                 2008-9 ANES
> *****************************************
> ****************************************/       
. 
. clear
{txt}
{com}. do "Data Cleaning - 2008 Panel.do"
{txt}
{com}. **********************************************************************
. **********************************************************************
. ***********************2008-9 ANES Panel Survey***********************
. **********************************************************************
. **********************************************************************
. **********************************************************************
. 
. 
. 
. 
. **********************************************************************
. ****************************Data Cleaning****************************
. **********************************************************************
. **********************************************************************
. **********************************************************************
. **********************************************************************
. clear
{txt}
{com}. set more off
{txt}
{com}. use "ANES 2008 Panel.dta"
{txt}
{com}. set more off
{txt}
{com}. 
. 
.                 
.                 ************************************
.                 *********Economic Assessments*******
.                 ************************************
. 
. *W1
.         *Economy vs. 2001
.         recode  W1T1 (-7=.) (-6=.) (-5=.) (-4=.) ///
>                 (1=5) (2=4) (3=3) (4=2) (5=1)   , gen(w1_econ2001)
{txt}(4009 differences between W1T1 and w1_econ2001)

{com}.                 
.                 
.         label var w1_econ2001 "Econ Better than 2001?"
{txt}
{com}.         label def bet 1 "Much Worse" 2 "Somewhat Worse" 3 "Same" 4 "Somewhat Better" 5 "Much Better" 
{txt}
{com}.         label values w1_econ2001 bet
{txt}
{com}.         
.         recode w1_econ2001 (1=1) (2=1) (3=2) (4=3) (5=3), gen(w1_econ2001_3)
{txt}(949 differences between w1_econ2001 and w1_econ2001_3)

{com}.         label def bet1 1 "Worse" 2 "Same" 3 "Better" 
{txt}
{com}.         label var w1_econ2001_3 "Econ Better than 2001 (W1)?"
{txt}
{com}.         label values w1_econ2001_3 bet1
{txt}
{com}. 
.         tab w1_econ2001

    {txt}Econ Better {c |}
     than 2001? {c |}      Freq.     Percent        Cum.
{hline 16}{c +}{hline 35}
     Much Worse {c |}{res}        668       41.31       41.31
{txt} Somewhat Worse {c |}{res}        558       34.51       75.82
{txt}           Same {c |}{res}        231       14.29       90.11
{txt}Somewhat Better {c |}{res}        126        7.79       97.90
{txt}    Much Better {c |}{res}         34        2.10      100.00
{txt}{hline 16}{c +}{hline 35}
          Total {c |}{res}      1,617      100.00
{txt}
{com}.         tab w1_econ2001_3

{txt}Econ Better {c |}
  than 2001 {c |}
      (W1)? {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
      Worse {c |}{res}      1,226       75.82       75.82
{txt}       Same {c |}{res}        231       14.29       90.11
{txt}     Better {c |}{res}        160        9.89      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,617      100.00
{txt}
{com}.         
.         *Deficit vs. 2001
.         recode W1T4 (-7=.) (-6=.) (-5=.) (-4=.) ///
>                 (1=5) (2=4) (3=3) (4=2) (5=1) , gen(w1_deficit2001)
{txt}(4026 differences between W1T4 and w1_deficit2001)

{com}.         
.         label var w1_deficit2001 "Deficit Better than 2001 (W1)?"
{txt}
{com}.         label values w1_deficit2001 bet
{txt}
{com}.         
.         recode w1_deficit2001 (1=1) (2=1) (3=2) (4=3) (5=3), gen(w1_deficit2001_3)
{txt}(727 differences between w1_deficit2001 and w1_deficit2001_3)

{com}.         label var w1_deficit2001_3 "Deficit Better than 2001 (W1)?"
{txt}
{com}.         label values w1_deficit2001_3 bet1
{txt}
{com}.         
.         tab w1_deficit2001

 {txt}Deficit Better {c |}
than 2001 (W1)? {c |}      Freq.     Percent        Cum.
{hline 16}{c +}{hline 35}
     Much Worse {c |}{res}        887       54.96       54.96
{txt} Somewhat Worse {c |}{res}        472       29.24       84.20
{txt}           Same {c |}{res}        214       13.26       97.46
{txt}Somewhat Better {c |}{res}         34        2.11       99.57
{txt}    Much Better {c |}{res}          7        0.43      100.00
{txt}{hline 16}{c +}{hline 35}
          Total {c |}{res}      1,614      100.00
{txt}
{com}.         tab w1_deficit2001_3

    {txt}Deficit {c |}
Better than {c |}
 2001 (W1)? {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
      Worse {c |}{res}      1,359       84.20       84.20
{txt}       Same {c |}{res}        214       13.26       97.46
{txt}     Better {c |}{res}         41        2.54      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,614      100.00
{txt}
{com}.         
.         *Economy vs. 2007
.         recode W1U1 (-7=.) (-6=.) (-5=.) (-4=.) ///
>                 (1=3) (2=2) (3=1), gen(w1_retro)
{txt}(3869 differences between W1U1 and w1_retro)

{com}.         label var w1_retro "Econ Better than 1yr Ago (W1)?"
{txt}
{com}.         label values w1_retro bet1
{txt}
{com}.         
.         gen w1_retro5 = .
{txt}(4240 missing values generated)

{com}.         replace w1_retro5 = 5 if w1_retro == 3 & W1U2 == 1
{txt}(12 real changes made)

{com}.         replace w1_retro5 = 4 if w1_retro == 3 & W1U2 == 2
{txt}(39 real changes made)

{com}.         replace w1_retro5 = 3 if w1_retro == 2
{txt}(371 real changes made)

{com}.         replace w1_retro5 = 2 if w1_retro == 1 & W1U3 == 2
{txt}(635 real changes made)

{com}.         replace w1_retro5 = 1 if w1_retro == 1 & W1U3 == 1
{txt}(556 real changes made)

{com}.         label var w1_retro5 "Econ Better than 1yr Ago (W1)?"
{txt}
{com}.         label values w1_retro5 bet
{txt}
{com}. 
.         tab w1_retro

{txt}Econ Better {c |}
   than 1yr {c |}
  Ago (W1)? {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
      Worse {c |}{res}      1,195       73.90       73.90
{txt}       Same {c |}{res}        371       22.94       96.85
{txt}     Better {c |}{res}         51        3.15      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,617      100.00
{txt}
{com}.         tab w1_retro5

    {txt}Econ Better {c |}
   than 1yr Ago {c |}
          (W1)? {c |}      Freq.     Percent        Cum.
{hline 16}{c +}{hline 35}
     Much Worse {c |}{res}        556       34.47       34.47
{txt} Somewhat Worse {c |}{res}        635       39.37       73.84
{txt}           Same {c |}{res}        371       23.00       96.84
{txt}Somewhat Better {c |}{res}         39        2.42       99.26
{txt}    Much Better {c |}{res}         12        0.74      100.00
{txt}{hline 16}{c +}{hline 35}
          Total {c |}{res}      1,613      100.00
{txt}
{com}.         
.         *Prospective
.         recode W1U4 (-7=.) (-6=.) (-5=.) (-4=.), gen(w1_prosp)
{txt}(2624 differences between W1U4 and w1_prosp)

{com}.         label var w1_prosp "Econ Worse in 1yr (W1)?"
{txt}
{com}.         label def wor 1 "Better" 2 "Same" 3 "Worse"
{txt}
{com}.         label values w1_prosp wor
{txt}
{com}.                 
.         gen w1_prosp5 = .
{txt}(4240 missing values generated)

{com}.         replace w1_prosp5 = 1  if w1_prosp == 3 & W1U5 == 1
{txt}(0 real changes made)

{com}.         replace w1_prosp5 = 2  if w1_prosp == 3 & W1U5 == 2
{txt}(0 real changes made)

{com}.         replace w1_prosp5 = 3  if w1_prosp == 2
{txt}(755 real changes made)

{com}.         replace w1_prosp5 = 4  if w1_prosp == 1 & W1U6 == 2
{txt}(0 real changes made)

{com}.         replace w1_prosp5 = 5  if w1_prosp == 1 & W1U6 == 1
{txt}(0 real changes made)

{com}.         label var w1_prosp5 "Econ Worse in 1yr (W1)?"
{txt}
{com}.         label def mwor 1 "Much Better" 2 "Somewhat Better" 3 "Same" ///
>                 4 "Somewhat Worse" 5 "Somewhat Worse" 
{txt}
{com}.         label values w1_prosp5 mwor
{txt}
{com}.         
.         tab w1_prosp 

 {txt}Econ Worse {c |}
     in 1yr {c |}
      (W1)? {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
     Better {c |}{res}        318       19.68       19.68
{txt}       Same {c |}{res}        755       46.72       66.40
{txt}      Worse {c |}{res}        543       33.60      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,616      100.00
{txt}
{com}.         tab w1_prosp5   

  {txt}Econ Worse in {c |}
      1yr (W1)? {c |}      Freq.     Percent        Cum.
{hline 16}{c +}{hline 35}
           Same {c |}{res}        755      100.00      100.00
{txt}{hline 16}{c +}{hline 35}
          Total {c |}{res}        755      100.00
{txt}
{com}. 
. *W6
.         *Economy vs. 2001
.         recode  W6T1 (-7=.) (-6=.) (-5=.) (-4=.) ///
>                 (1=5) (2=4) (3=3) (4=2) (5=1), gen(w6_econ2001)
{txt}(4117 differences between W6T1 and w6_econ2001)

{com}.         label var w6_econ2001 "Econ Better than 2001 (W6)?"
{txt}
{com}.         label values w6_econ2001 bet
{txt}
{com}.         
.         recode w6_econ2001 (1=1) (2=1) (3=2) (4=3) (5=3), gen(w6_econ2001_3)
{txt}(560 differences between w6_econ2001 and w6_econ2001_3)

{com}.         label var w6_econ2001_3 "Economy Better than 2001? (W6)"
{txt}
{com}.         label values w6_econ2001_3 bet1
{txt}
{com}. 
.         tab w6_econ2001

    {txt}Econ Better {c |}
than 2001 (W6)? {c |}      Freq.     Percent        Cum.
{hline 16}{c +}{hline 35}
     Much Worse {c |}{res}        860       60.56       60.56
{txt} Somewhat Worse {c |}{res}        394       27.75       88.31
{txt}           Same {c |}{res}        123        8.66       96.97
{txt}Somewhat Better {c |}{res}         34        2.39       99.37
{txt}    Much Better {c |}{res}          9        0.63      100.00
{txt}{hline 16}{c +}{hline 35}
          Total {c |}{res}      1,420      100.00
{txt}
{com}.         tab w6_econ2001_3

    {txt}Economy {c |}
Better than {c |}
 2001? (W6) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
      Worse {c |}{res}      1,254       88.31       88.31
{txt}       Same {c |}{res}        123        8.66       96.97
{txt}     Better {c |}{res}         43        3.03      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,420      100.00
{txt}
{com}.         
.         *Deficit vs. 2001
.         recode W6T4 (-7=.) (-6=.) (-5=.) (-4=.) ///
>                 (1=5) (2=4) (3=3) (4=2) (5=1), gen(w6_deficit2001)
{txt}(4107 differences between W6T4 and w6_deficit2001)

{com}.         label var w6_deficit2001 "Deficit Better than 2001? (W6)"
{txt}
{com}.         label values w6_deficit2001 bet
{txt}
{com}.         
.         recode w6_deficit2001 (1=1) (2=1) (3=2) (4=3) (5=3), gen(w6_deficit2001_3)
{txt}(523 differences between w6_deficit2001 and w6_deficit2001_3)

{com}.         label var w6_deficit2001_3 "Deficit Better than 2001? (W6)"
{txt}
{com}.         label values w6_deficit2001_3 bet1
{txt}
{com}.         
.         tab w6_deficit2001

 {txt}Deficit Better {c |}
than 2001? (W6) {c |}      Freq.     Percent        Cum.
{hline 16}{c +}{hline 35}
     Much Worse {c |}{res}        897       63.17       63.17
{txt} Somewhat Worse {c |}{res}        368       25.92       89.08
{txt}           Same {c |}{res}        133        9.37       98.45
{txt}Somewhat Better {c |}{res}         18        1.27       99.72
{txt}    Much Better {c |}{res}          4        0.28      100.00
{txt}{hline 16}{c +}{hline 35}
          Total {c |}{res}      1,420      100.00
{txt}
{com}.         tab w6_deficit2001_3

    {txt}Deficit {c |}
Better than {c |}
 2001? (W6) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
      Worse {c |}{res}      1,265       89.08       89.08
{txt}       Same {c |}{res}        133        9.37       98.45
{txt}     Better {c |}{res}         22        1.55      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,420      100.00
{txt}
{com}.         
.         *Economy vs. 2007
.         recode W6U1 (-7=.) (-6=.) (-5=.) (-4=.) ///
>                 (1=3) (2=2) (3=1) , gen(w6_retro)
{txt}(4063 differences between W6U1 and w6_retro)

{com}.         label var w6_retro "Econ Better than 1yr Ago (W6)?"
{txt}
{com}.         label values w6_retro bet1
{txt}
{com}.         
.         gen w6_retro5 = .
{txt}(4240 missing values generated)

{com}.         replace w6_retro5 = 5 if w6_retro == 3 & W6U2 == 1
{txt}(4 real changes made)

{com}.         replace w6_retro5 = 4 if w6_retro == 3 & W6U2 == 2
{txt}(15 real changes made)

{com}.         replace w6_retro5 = 3 if w6_retro == 2
{txt}(177 real changes made)

{com}.         replace w6_retro5 = 2 if w6_retro == 1 & W6U3 == 2
{txt}(430 real changes made)

{com}.         replace w6_retro5 = 1 if w6_retro == 1 & W6U3 == 1
{txt}(793 real changes made)

{com}.         label var w6_retro5 "Econ Better than 1yr Ago (W6)?"
{txt}
{com}.         label values w6_retro5 bet
{txt}
{com}. 
.         tab w6_retro

{txt}Econ Better {c |}
   than 1yr {c |}
  Ago (W6)? {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
      Worse {c |}{res}      1,224       86.20       86.20
{txt}       Same {c |}{res}        177       12.46       98.66
{txt}     Better {c |}{res}         19        1.34      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,420      100.00
{txt}
{com}.         tab w6_retro5

    {txt}Econ Better {c |}
   than 1yr Ago {c |}
          (W6)? {c |}      Freq.     Percent        Cum.
{hline 16}{c +}{hline 35}
     Much Worse {c |}{res}        793       55.88       55.88
{txt} Somewhat Worse {c |}{res}        430       30.30       86.19
{txt}           Same {c |}{res}        177       12.47       98.66
{txt}Somewhat Better {c |}{res}         15        1.06       99.72
{txt}    Much Better {c |}{res}          4        0.28      100.00
{txt}{hline 16}{c +}{hline 35}
          Total {c |}{res}      1,419      100.00
{txt}
{com}.         
.         *Prospective
.         recode W6U4 (-7=.) (-6=.) (-5=.) (-4=.), gen(w6_prosp)
{txt}(2821 differences between W6U4 and w6_prosp)

{com}.         label var w6_prosp "Econ Worse in 1yr (W6)?"
{txt}
{com}.         label values w6_prosp wor
{txt}
{com}.                 
.         gen w6_prosp5 = .
{txt}(4240 missing values generated)

{com}.         replace w6_prosp5 = 1  if w6_prosp == 1 & W6U5 == 1
{txt}(34 real changes made)

{com}.         replace w6_prosp5 = 2  if w6_prosp == 1 & W6U5 == 2
{txt}(243 real changes made)

{com}.         replace w6_prosp5 = 3  if w6_prosp == 2
{txt}(561 real changes made)

{com}.         replace w6_prosp5 = 4  if w6_prosp == 3 & W6U6 == 2
{txt}(311 real changes made)

{com}.         replace w6_prosp5 = 5  if w6_prosp == 3 & W6U6 == 1
{txt}(269 real changes made)

{com}.         label var w6_prosp5 "Econ Worse in 1yr (W6)?"
{txt}
{com}.         label values w6_prosp5 mwor
{txt}
{com}.         
.         tab w6_prosp 

 {txt}Econ Worse {c |}
     in 1yr {c |}
      (W6)? {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
     Better {c |}{res}        277       19.52       19.52
{txt}       Same {c |}{res}        561       39.53       59.06
{txt}      Worse {c |}{res}        581       40.94      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,419      100.00
{txt}
{com}.         tab w6_prosp5   

  {txt}Econ Worse in {c |}
      1yr (W6)? {c |}      Freq.     Percent        Cum.
{hline 16}{c +}{hline 35}
    Much Better {c |}{res}         34        2.40        2.40
{txt}Somewhat Better {c |}{res}        243       17.14       19.53
{txt}           Same {c |}{res}        561       39.56       59.10
{txt} Somewhat Worse {c |}{res}        311       21.93       81.03
{txt} Somewhat Worse {c |}{res}        269       18.97      100.00
{txt}{hline 16}{c +}{hline 35}
          Total {c |}{res}      1,418      100.00
{txt}
{com}. 
. *W10
.         *Economy vs. 2007
.         label def reverse 1 "Worse" 2 "Same" 3 "Better" 
{txt}
{com}.         label def reverse5 1 "Much Worse" 2 "Somewhat Worse" 3 "Same" 4 "Somewhat Better" 5 "Much Better" 
{txt}
{com}.         recode W10U1 (-7=.) (-6=.) (-5=.) (-4=.) (1=3) (2=2) (3=1)      , gen(w10_retro)
{txt}(4100 differences between W10U1 and w10_retro)

{com}.         label var w10_retro "Econ Better than 1yr Ago (W10)?"
{txt}
{com}.         label values w10_retro reverse
{txt}
{com}.                 
.         gen w10_retro5 = .
{txt}(4240 missing values generated)

{com}.         replace w10_retro5 = 5 if w10_retro == 3 & W10U2 == 1
{txt}(8 real changes made)

{com}.         replace w10_retro5 = 4 if w10_retro == 3 & W10U2 == 2
{txt}(6 real changes made)

{com}.         replace w10_retro5 = 3 if w10_retro == 2
{txt}(140 real changes made)

{com}.         replace w10_retro5 = 2 if w10_retro == 1 & W10U3 == 2
{txt}(417 real changes made)

{com}.         replace w10_retro5 = 1 if w10_retro == 1 & W10U3 == 1
{txt}(2054 real changes made)

{com}.         label var w10_retro5 "Econ Worse than 1yr Ago (W10)?"
{txt}
{com}.         label values w10_retro5 reverse5
{txt}
{com}.         
.         tab w10_retro

{txt}Econ Better {c |}
   than 1yr {c |}
 Ago (W10)? {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
      Worse {c |}{res}      2,472       94.14       94.14
{txt}       Same {c |}{res}        140        5.33       99.47
{txt}     Better {c |}{res}         14        0.53      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,626      100.00
{txt}
{com}.         tab w10_retro5

{txt}Econ Worse than {c |}
 1yr Ago (W10)? {c |}      Freq.     Percent        Cum.
{hline 16}{c +}{hline 35}
     Much Worse {c |}{res}      2,054       78.25       78.25
{txt} Somewhat Worse {c |}{res}        417       15.89       94.13
{txt}           Same {c |}{res}        140        5.33       99.47
{txt}Somewhat Better {c |}{res}          6        0.23       99.70
{txt}    Much Better {c |}{res}          8        0.30      100.00
{txt}{hline 16}{c +}{hline 35}
          Total {c |}{res}      2,625      100.00
{txt}
{com}.         
.         *Prospective
.         recode W10U4 (-7=.) (-6=.) (-5=.) (-4=.), gen(w10_prosp)
{txt}(1617 differences between W10U4 and w10_prosp)

{com}.         label var w10_prosp "Econ Worse in 1yr (W10)?"
{txt}
{com}.         label values w10_prosp wor
{txt}
{com}.                 
.         gen w10_prosp5 = .
{txt}(4240 missing values generated)

{com}.         replace w10_prosp5 = 1  if w10_prosp == 1 & W10U5 == 1
{txt}(142 real changes made)

{com}.         replace w10_prosp5 = 2  if w10_prosp == 1 & W10U5 == 2
{txt}(696 real changes made)

{com}.         replace w10_prosp5 = 3  if w10_prosp == 2
{txt}(1099 real changes made)

{com}.         replace w10_prosp5 = 4  if w10_prosp == 3 & W10U6 == 2
{txt}(333 real changes made)

{com}.         replace w10_prosp5 = 5  if w10_prosp == 3 & W10U6 == 1
{txt}(352 real changes made)

{com}.         label var w10_prosp5 "Econ Worse in 1yr (W10)?"
{txt}
{com}.         label values w10_prosp5 mwor
{txt}
{com}.         
.         tab w10_prosp 

 {txt}Econ Worse {c |}
     in 1yr {c |}
     (W10)? {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
     Better {c |}{res}        839       31.99       31.99
{txt}       Same {c |}{res}      1,099       41.90       73.88
{txt}      Worse {c |}{res}        685       26.12      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,623      100.00
{txt}
{com}.         tab w10_prosp5  

  {txt}Econ Worse in {c |}
     1yr (W10)? {c |}      Freq.     Percent        Cum.
{hline 16}{c +}{hline 35}
    Much Better {c |}{res}        142        5.42        5.42
{txt}Somewhat Better {c |}{res}        696       26.54       31.96
{txt}           Same {c |}{res}      1,099       41.91       73.87
{txt} Somewhat Worse {c |}{res}        333       12.70       86.58
{txt} Somewhat Worse {c |}{res}        352       13.42      100.00
{txt}{hline 16}{c +}{hline 35}
          Total {c |}{res}      2,622      100.00
{txt}
{com}. 
. *W11
.         *Economy vs. 2001
.         recode  W11U1 (-7=.) (-6=.) (-5=.) (-4=.) (1=5) (2=4) (3=3) (4=2) (5=1) , gen(w11_econ2001)
{txt}(4142 differences between W11U1 and w11_econ2001)

{com}.         label var w11_econ2001 "Econ Worse than 2001 (W11)?"
{txt}
{com}.         label values w11_econ2001 reverse5
{txt}
{com}.         
.         recode w11_econ2001 (1=1) (2=1) (3=2) (4=3) (5=3), gen(w11_econ2001_3)
{txt}(588 differences between w11_econ2001 and w11_econ2001_3)

{com}.         label var w11_econ2001_3 "Econ Worse than 2001 (W11)?"
{txt}
{com}.         label values w11_econ2001_3 reverse
{txt}
{com}. 
.         tab w11_econ2001

{txt}Econ Worse than {c |}
    2001 (W11)? {c |}      Freq.     Percent        Cum.
{hline 16}{c +}{hline 35}
     Much Worse {c |}{res}      2,074       77.91       77.91
{txt} Somewhat Worse {c |}{res}        463       17.39       95.30
{txt}           Same {c |}{res}         98        3.68       98.99
{txt}Somewhat Better {c |}{res}         17        0.64       99.62
{txt}    Much Better {c |}{res}         10        0.38      100.00
{txt}{hline 16}{c +}{hline 35}
          Total {c |}{res}      2,662      100.00
{txt}
{com}.         tab w11_econ2001_3

 {txt}Econ Worse {c |}
  than 2001 {c |}
     (W11)? {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
      Worse {c |}{res}      2,537       95.30       95.30
{txt}       Same {c |}{res}         98        3.68       98.99
{txt}     Better {c |}{res}         27        1.01      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,662      100.00
{txt}
{com}. 
.         
.         *Deficit vs. 2001
.         recode W11U4 (-7=.) (-6=.) (-5=.) (-4=.) (1=5) (2=4) (3=3) (4=2) (5=1), gen(w11_deficit2001)
{txt}(4138 differences between W11U4 and w11_deficit2001)

{com}.         label var w11_deficit2001 "Deficit Worse than 2001 (W11)?"
{txt}
{com}.         label values w11_deficit2001 reverse5
{txt}
{com}.         
.         recode w11_deficit2001 (1=1) (2=1) (3=2) (4=3) (5=3), gen(w11_deficit2001_3)
{txt}(556 differences between w11_deficit2001 and w11_deficit2001_3)

{com}.         label var w11_deficit2001_3 "Deficit Worse than 2001 (W11)?"
{txt}
{com}.         label values w11_deficit2001_3 reverse
{txt}
{com}.         
.         tab w11_deficit2001

  {txt}Deficit Worse {c |}
      than 2001 {c |}
         (W11)? {c |}      Freq.     Percent        Cum.
{hline 16}{c +}{hline 35}
     Much Worse {c |}{res}      2,106       79.11       79.11
{txt} Somewhat Worse {c |}{res}        429       16.12       95.23
{txt}           Same {c |}{res}        102        3.83       99.06
{txt}Somewhat Better {c |}{res}         17        0.64       99.70
{txt}    Much Better {c |}{res}          8        0.30      100.00
{txt}{hline 16}{c +}{hline 35}
          Total {c |}{res}      2,662      100.00
{txt}
{com}.         tab w11_deficit2001_3

    {txt}Deficit {c |}
 Worse than {c |}
2001 (W11)? {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
      Worse {c |}{res}      2,535       95.23       95.23
{txt}       Same {c |}{res}        102        3.83       99.06
{txt}     Better {c |}{res}         25        0.94      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,662      100.00
{txt}
{com}. 
.         *Economy vs. 2007
.         recode W11V1 (-7=.) (-6=.) (-5=.) (-4=.) (1=3) (2=2) (3=1)      , gen(w11_retro)
{txt}(4080 differences between W11V1 and w11_retro)

{com}.         label var w11_retro "Econ Worse than 1yr Ago (W11)?"
{txt}
{com}.         label values w11_retro reverse
{txt}
{com}.         
.         gen w11_retro5 = .
{txt}(4240 missing values generated)

{com}.         replace w11_retro5 = 1 if w11_retro == 1 & W11V3 == 1
{txt}(2058 real changes made)

{com}.         replace w11_retro5 = 2 if w11_retro == 1 & W11V3 == 2
{txt}(426 real changes made)

{com}.         replace w11_retro5 = 3 if w11_retro == 2
{txt}(160 real changes made)

{com}.         replace w11_retro5 = 4 if w11_retro == 3 & W11V2 == 2
{txt}(11 real changes made)

{com}.         replace w11_retro5 = 5 if w11_retro == 3 & W11V2 == 1
{txt}(6 real changes made)

{com}.         label var w11_retro5 "Econ Worse than 1yr Ago (W11)?"
{txt}
{com}.         label values w11_retro5 reverse5
{txt}
{com}. 
.         tab w11_retro

 {txt}Econ Worse {c |}
   than 1yr {c |}
 Ago (W11)? {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
      Worse {c |}{res}      2,486       93.35       93.35
{txt}       Same {c |}{res}        160        6.01       99.36
{txt}     Better {c |}{res}         17        0.64      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,663      100.00
{txt}
{com}.         tab w11_retro5

{txt}Econ Worse than {c |}
 1yr Ago (W11)? {c |}      Freq.     Percent        Cum.
{hline 16}{c +}{hline 35}
     Much Worse {c |}{res}      2,058       77.34       77.34
{txt} Somewhat Worse {c |}{res}        426       16.01       93.35
{txt}           Same {c |}{res}        160        6.01       99.36
{txt}Somewhat Better {c |}{res}         11        0.41       99.77
{txt}    Much Better {c |}{res}          6        0.23      100.00
{txt}{hline 16}{c +}{hline 35}
          Total {c |}{res}      2,661      100.00
{txt}
{com}. 
.         *Prospective
.         recode W11V4 (-7=.) (-6=.) (-5=.) (-4=.), gen(w11_prosp)
{txt}(1576 differences between W11V4 and w11_prosp)

{com}.         label var w11_prosp "Econ Worse in 1yr (W11)?"
{txt}
{com}.         label values w11_prosp wor
{txt}
{com}.                 
.         gen w11_prosp5 = .
{txt}(4240 missing values generated)

{com}.         replace w11_prosp5 = 1  if w11_prosp == 1 & W11V5 == 1
{txt}(133 real changes made)

{com}.         replace w11_prosp5 = 2  if w11_prosp == 1 & W11V5 == 2
{txt}(867 real changes made)

{com}.         replace w11_prosp5 = 3  if w11_prosp == 2
{txt}(1061 real changes made)

{com}.         replace w11_prosp5 = 4  if w11_prosp == 3 & W11V6 == 2
{txt}(311 real changes made)

{com}.         replace w11_prosp5 = 5  if w11_prosp == 3 & W11V6 == 1
{txt}(291 real changes made)

{com}.         label var w11_prosp5 "Econ Worse in 1yr (W11)?"
{txt}
{com}.         label values w11_prosp5 mwor
{txt}
{com}.         
.         tab w11_prosp 

 {txt}Econ Worse {c |}
     in 1yr {c |}
     (W11)? {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
     Better {c |}{res}      1,001       37.58       37.58
{txt}       Same {c |}{res}      1,061       39.83       77.40
{txt}      Worse {c |}{res}        602       22.60      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,664      100.00
{txt}
{com}.         tab w11_prosp5  

  {txt}Econ Worse in {c |}
     1yr (W11)? {c |}      Freq.     Percent        Cum.
{hline 16}{c +}{hline 35}
    Much Better {c |}{res}        133        4.99        4.99
{txt}Somewhat Better {c |}{res}        867       32.56       37.55
{txt}           Same {c |}{res}      1,061       39.84       77.39
{txt} Somewhat Worse {c |}{res}        311       11.68       89.07
{txt} Somewhat Worse {c |}{res}        291       10.93      100.00
{txt}{hline 16}{c +}{hline 35}
          Total {c |}{res}      2,663      100.00
{txt}
{com}. 
. *W17
.         *Deficit vs. 2009
.         recode W17U4 (-7=.) (-6=.) (-5=.) (-4=.) (1=5) (2=4) (3=3) (4=2) (5=1), gen(w17_deficit2001)
{txt}(3867 differences between W17U4 and w17_deficit2001)

{com}.         label var w17_deficit2001 "Deficit Worse than Jan. 2009 (W17)?"
{txt}
{com}.         label values w17_deficit2001 reverse5
{txt}
{com}.         
.         recode w17_deficit2001 (1=1) (2=1) (3=2) (4=3) (5=3), gen(w17_deficit2001_3)
{txt}(1197 differences between w17_deficit2001 and w17_deficit2001_3)

{com}.         label var w17_deficit2001_3 "Deficit Worse Jan. 2009 (W17)?"
{txt}
{com}.         label values w17_deficit2001_3 reverse
{txt}
{com}.         
.         tab w17_deficit2001

  {txt}Deficit Worse {c |}
 than Jan. 2009 {c |}
         (W17)? {c |}      Freq.     Percent        Cum.
{hline 16}{c +}{hline 35}
     Much Worse {c |}{res}      1,193       49.92       49.92
{txt} Somewhat Worse {c |}{res}        755       31.59       81.51
{txt}           Same {c |}{res}        373       15.61       97.11
{txt}Somewhat Better {c |}{res}         64        2.68       99.79
{txt}    Much Better {c |}{res}          5        0.21      100.00
{txt}{hline 16}{c +}{hline 35}
          Total {c |}{res}      2,390      100.00
{txt}
{com}.         tab w17_deficit2001_3

    {txt}Deficit {c |}
 Worse Jan. {c |}
2009 (W17)? {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
      Worse {c |}{res}      1,948       81.51       81.51
{txt}       Same {c |}{res}        373       15.61       97.11
{txt}     Better {c |}{res}         69        2.89      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,390      100.00
{txt}
{com}.         
.         gen def_knowl17 = .
{txt}(4240 missing values generated)

{com}.         replace def_knowl17 = 1 if w17_deficit2001_3 == 1
{txt}(1948 real changes made)

{com}.         replace def_knowl17 = 0 if w17_deficit2001_3 >=2 & w17_deficit2001_3 <=3
{txt}(442 real changes made)

{com}.         label var def_knowl17 "Deficit Knowledge" 
{txt}
{com}.         label def kno 1 "Correct" 0 "Incorrect" 
{txt}
{com}.         label values def_knowl17 kno
{txt}
{com}.         
.         
.         *Economy vs. Jan. 2009
.         recode W17V1 (-7=.) (-6=.) (-5=.) (-4=.) (1=3) (2=2) (3=1), gen(w17_retro)
{txt}(3479 differences between W17V1 and w17_retro)

{com}.         label var w17_retro "Econ Worse than Jan. 2009 (W17)?"
{txt}
{com}.         label values w17_retro reverse
{txt}
{com}.         
.         gen w17_retro5 = .
{txt}(4240 missing values generated)

{com}.         replace w17_retro5 = 1 if w17_retro == 1 & W17V3 == 1
{txt}(663 real changes made)

{com}.         replace w17_retro5 = 2 if w17_retro == 1 & W17V3 == 2
{txt}(602 real changes made)

{com}.         replace w17_retro5 = 3 if w17_retro == 2
{txt}(761 real changes made)

{com}.         replace w17_retro5 = 4 if w17_retro == 3 & W17V2 == 2
{txt}(345 real changes made)

{com}.         replace w17_retro5 = 5 if w17_retro == 3 & W17V2 == 1
{txt}(18 real changes made)

{com}.         label var w17_retro5 "Econ Worse since Jan. 2009 (W17)?"
{txt}
{com}.         label values w17_retro5 reverse5
{txt}
{com}. 
.         tab w17_retro

 {txt}Econ Worse {c |}
  than Jan. {c |}
2009 (W17)? {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
      Worse {c |}{res}      1,266       52.97       52.97
{txt}       Same {c |}{res}        761       31.84       84.81
{txt}     Better {c |}{res}        363       15.19      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,390      100.00
{txt}
{com}.         tab w17_retro5

     {txt}Econ Worse {c |}
since Jan. 2009 {c |}
         (W17)? {c |}      Freq.     Percent        Cum.
{hline 16}{c +}{hline 35}
     Much Worse {c |}{res}        663       27.75       27.75
{txt} Somewhat Worse {c |}{res}        602       25.20       52.95
{txt}           Same {c |}{res}        761       31.85       84.81
{txt}Somewhat Better {c |}{res}        345       14.44       99.25
{txt}    Much Better {c |}{res}         18        0.75      100.00
{txt}{hline 16}{c +}{hline 35}
          Total {c |}{res}      2,389      100.00
{txt}
{com}.         
.         *Prospective
.         recode W17V4 (-7=.) (-6=.) (-5=.) (-4=.), gen(w17_prosp)
{txt}(1851 differences between W17V4 and w17_prosp)

{com}.         label var w17_prosp "Econ Worse in 1yr (W17)?"
{txt}
{com}.         label values w17_prosp wor
{txt}
{com}.                 
.         gen w17_prosp5 = .
{txt}(4240 missing values generated)

{com}.         replace w17_prosp5 = 1  if w17_prosp == 1 & W17V5 == 1
{txt}(259 real changes made)

{com}.         replace w17_prosp5 = 2  if w17_prosp == 1 & W17V5 == 2
{txt}(987 real changes made)

{com}.         replace w17_prosp5 = 3  if w17_prosp == 2
{txt}(633 real changes made)

{com}.         replace w17_prosp5 = 4  if w17_prosp == 3 & W17V6 == 2
{txt}(255 real changes made)

{com}.         replace w17_prosp5 = 5  if w17_prosp == 3 & W17V6 == 1
{txt}(253 real changes made)

{com}.         label var w17_prosp5 "Econ Worse in 1yr (W17)"
{txt}
{com}.         label values w17_prosp5 mwor
{txt}
{com}.         
.         tab w17_prosp 

 {txt}Econ Worse {c |}
     in 1yr {c |}
     (W17)? {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
     Better {c |}{res}      1,248       52.24       52.24
{txt}       Same {c |}{res}        633       26.50       78.74
{txt}      Worse {c |}{res}        508       21.26      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,389      100.00
{txt}
{com}.         tab w17_prosp5  

  {txt}Econ Worse in {c |}
      1yr (W17) {c |}      Freq.     Percent        Cum.
{hline 16}{c +}{hline 35}
    Much Better {c |}{res}        259       10.85       10.85
{txt}Somewhat Better {c |}{res}        987       41.35       52.20
{txt}           Same {c |}{res}        633       26.52       78.72
{txt} Somewhat Worse {c |}{res}        255       10.68       89.40
{txt} Somewhat Worse {c |}{res}        253       10.60      100.00
{txt}{hline 16}{c +}{hline 35}
          Total {c |}{res}      2,387      100.00
{txt}
{com}. 
. 
. *W19
.         *Deficit vs. 2009
.         recode W19U4 (-7=.) (-6=.) (-5=.) (-4=.) (1=5) (2=4) (3=3) (4=2) (5=1), gen(w19_deficit2001)
{txt}(3847 differences between W19U4 and w19_deficit2001)

{com}.         label var w19_deficit2001 "Deficit Worse than Jan. 2009 (W19)?"
{txt}
{com}.         label values w19_deficit2001 reverse5
{txt}
{com}.         
.         recode w19_deficit2001 (1=1) (2=1) (3=2) (4=3) (5=3), gen(w19_deficit2001_3)
{txt}(1263 differences between w19_deficit2001 and w19_deficit2001_3)

{com}.         label var w19_deficit2001_3 "Deficit Worse Jan. 2009 (W19)?"
{txt}
{com}.         label values w19_deficit2001_3 reverse
{txt}
{com}.         
.         tab w19_deficit2001

  {txt}Deficit Worse {c |}
 than Jan. 2009 {c |}
         (W19)? {c |}      Freq.     Percent        Cum.
{hline 16}{c +}{hline 35}
     Much Worse {c |}{res}      1,122       47.04       47.04
{txt} Somewhat Worse {c |}{res}        779       32.66       79.71
{txt}           Same {c |}{res}        393       16.48       96.18
{txt}Somewhat Better {c |}{res}         81        3.40       99.58
{txt}    Much Better {c |}{res}         10        0.42      100.00
{txt}{hline 16}{c +}{hline 35}
          Total {c |}{res}      2,385      100.00
{txt}
{com}.         tab w19_deficit2001_3

    {txt}Deficit {c |}
 Worse Jan. {c |}
2009 (W19)? {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
      Worse {c |}{res}      1,901       79.71       79.71
{txt}       Same {c |}{res}        393       16.48       96.18
{txt}     Better {c |}{res}         91        3.82      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,385      100.00
{txt}
{com}.         
.         gen def_knowl19 = . 
{txt}(4240 missing values generated)

{com}.         replace def_knowl19 = 1 if w19_deficit2001_3 == 1
{txt}(1901 real changes made)

{com}.         replace def_knowl19 = 0 if w19_deficit2001_3 >=2 & w19_deficit2001_3 <=3
{txt}(484 real changes made)

{com}.         label var def_knowl19 "Deficit Knowledge" 
{txt}
{com}.         label values def_knowl19 kno
{txt}
{com}.         
.         
.         *Economy vs. Jan. 2009
.         recode W19V1 (-7=.) (-6=.) (-5=.) (-4=.) (1=3) (2=2) (3=1)      , gen(w19_retro)
{txt}(3498 differences between W19V1 and w19_retro)

{com}.         label var w19_retro "Econ Worse than Jan. 2009 (W19)?"
{txt}
{com}.         label values w19_retro reverse
{txt}
{com}.         
.         gen w19_retro5 = .
{txt}(4240 missing values generated)

{com}.         replace w19_retro5 = 1 if w19_retro == 1 & W19V3 == 1
{txt}(661 real changes made)

{com}.         replace w19_retro5 = 2 if w19_retro == 1 & W19V3 == 2
{txt}(688 real changes made)

{com}.         replace w19_retro5 = 3 if w19_retro == 2
{txt}(742 real changes made)

{com}.         replace w19_retro5 = 4 if w19_retro == 3 & W19V2 == 2
{txt}(280 real changes made)

{com}.         replace w19_retro5 = 5 if w19_retro == 3 & W19V2 == 1
{txt}(15 real changes made)

{com}.         label var w19_retro5 "Econ Worse since Jan. 2009 (W19)?"
{txt}
{com}.         label values w19_retro5 reverse5
{txt}
{com}. 
.         tab w19_retro

 {txt}Econ Worse {c |}
  than Jan. {c |}
2009 (W19)? {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
      Worse {c |}{res}      1,349       56.54       56.54
{txt}       Same {c |}{res}        742       31.10       87.64
{txt}     Better {c |}{res}        295       12.36      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,386      100.00
{txt}
{com}.         tab w19_retro5

     {txt}Econ Worse {c |}
since Jan. 2009 {c |}
         (W19)? {c |}      Freq.     Percent        Cum.
{hline 16}{c +}{hline 35}
     Much Worse {c |}{res}        661       27.70       27.70
{txt} Somewhat Worse {c |}{res}        688       28.83       56.54
{txt}           Same {c |}{res}        742       31.10       87.64
{txt}Somewhat Better {c |}{res}        280       11.74       99.37
{txt}    Much Better {c |}{res}         15        0.63      100.00
{txt}{hline 16}{c +}{hline 35}
          Total {c |}{res}      2,386      100.00
{txt}
{com}.         
.         *Prospective
.         recode W19V4 (-7=.) (-6=.) (-5=.) (-4=.), gen(w19_prosp)
{txt}(1855 differences between W19V4 and w19_prosp)

{com}.         label var w19_prosp "Econ Worse in 1yr (W19)?"
{txt}
{com}.         label values w19_prosp wor
{txt}
{com}.                 
.         gen w19_prosp5 = .
{txt}(4240 missing values generated)

{com}.         replace w19_prosp5 = 1  if w19_prosp == 1 & W19V5 == 1
{txt}(194 real changes made)

{com}.         replace w19_prosp5 = 2  if w19_prosp == 1 & W19V5 == 2
{txt}(919 real changes made)

{com}.         replace w19_prosp5 = 3  if w19_prosp == 2
{txt}(689 real changes made)

{com}.         replace w19_prosp5 = 4  if w19_prosp == 3 & W19V6 == 2
{txt}(286 real changes made)

{com}.         replace w19_prosp5 = 5  if w19_prosp == 3 & W19V6 == 1
{txt}(297 real changes made)

{com}.         label var w19_prosp5 "Econ Worse in 1yr (W19)"
{txt}
{com}.         label values w19_prosp5 mwor
{txt}
{com}.         
.         tab w19_prosp 

 {txt}Econ Worse {c |}
     in 1yr {c |}
     (W19)? {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
     Better {c |}{res}      1,113       46.67       46.67
{txt}       Same {c |}{res}        689       28.89       75.56
{txt}      Worse {c |}{res}        583       24.44      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,385      100.00
{txt}
{com}.         tab w19_prosp5  

  {txt}Econ Worse in {c |}
      1yr (W19) {c |}      Freq.     Percent        Cum.
{hline 16}{c +}{hline 35}
    Much Better {c |}{res}        194        8.13        8.13
{txt}Somewhat Better {c |}{res}        919       38.53       46.67
{txt}           Same {c |}{res}        689       28.89       75.56
{txt} Somewhat Worse {c |}{res}        286       11.99       87.55
{txt} Somewhat Worse {c |}{res}        297       12.45      100.00
{txt}{hline 16}{c +}{hline 35}
          Total {c |}{res}      2,385      100.00
{txt}
{com}.         
.         recode W19WU13 (1=1) (2=0) (-7=.) (-6=.) (-5=.), gen(obamarecession)
{txt}(2976 differences between W19WU13 and obamarecession)

{com}.         label var obamarecession "Obama Helped End Recession?"
{txt}
{com}.         label def oba 1 "Obama Helped End Recession" 0 "Obama Has Not Helped End It"
{txt}
{com}.         label values obamarecession oba
{txt}
{com}.         tab obamarecession

{txt}Obama Helped End Recession? {c |}      Freq.     Percent        Cum.
{hline 28}{c +}{hline 35}
Obama Has Not Helped End It {c |}{res}      1,111       46.78       46.78
{txt} Obama Helped End Recession {c |}{res}      1,264       53.22      100.00
{txt}{hline 28}{c +}{hline 35}
                      Total {c |}{res}      2,375      100.00
{txt}
{com}. 
. 
. *Relationships Over Time*
.         pwcorr  w1_retro5 w6_retro5 w10_retro5 w11_retro5       w17_retro5 w19_retro5, sig

             {txt}{c |} w1_ret~5 w6_ret~5 w10_re~5 w11_re~5 w17_re~5 w19_re~5
{hline 13}{c +}{hline 54}
   w1_retro5 {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
   w6_retro5 {c |} {res}  0.4942   1.0000 
             {txt}{c |}{res}   0.0000
             {txt}{c |}
  w10_retro5 {c |} {res}  0.3713   0.4071   1.0000 
             {txt}{c |}{res}   0.0000   0.0000
             {txt}{c |}
  w11_retro5 {c |} {res}  0.3262   0.3808   0.4568   1.0000 
             {txt}{c |}{res}   0.0000   0.0000   0.0000
             {txt}{c |}
  w17_retro5 {c |} {res} -0.0416  -0.0272  -0.0304  -0.0032   1.0000 
             {txt}{c |}{res}   0.1413   0.3512   0.1522   0.8775
             {txt}{c |}
  w19_retro5 {c |} {res} -0.1086  -0.0464  -0.0809  -0.0381   0.5396   1.0000 
             {txt}{c |}{res}   0.0001   0.1141   0.0001   0.0707   0.0000
             {txt}{c |}

{com}.         pwcorr w1_prosp5         w6_prosp5       w10_prosp5      w11_prosp5  w17_prosp5          w19_prosp5, sig

             {txt}{c |} w1_pro~5 w6_pro~5 w10_pr~5 w11_pr~5 w17_pr~5 w19_pr~5
{hline 13}{c +}{hline 54}
   w1_prosp5 {c |} {res}       . 
             {txt}{c |}
             {c |}
   w6_prosp5 {c |} {res}       .   1.0000 
             {txt}{c |}{res}        .
             {txt}{c |}
  w10_prosp5 {c |} {res}       .   0.4208   1.0000 
             {txt}{c |}{res}        .   0.0000
             {txt}{c |}
  w11_prosp5 {c |} {res}       .   0.2612   0.3957   1.0000 
             {txt}{c |}{res}        .   0.0000   0.0000
             {txt}{c |}
  w17_prosp5 {c |} {res}       .   0.2122   0.2643   0.4386   1.0000 
             {txt}{c |}{res}        .   0.0000   0.0000   0.0000
             {txt}{c |}
  w19_prosp5 {c |} {res}       .   0.1749   0.2107   0.4112   0.6670   1.0000 
             {txt}{c |}{res}        .   0.0000   0.0000   0.0000   0.0000
             {txt}{c |}

{com}.         
.         
.                 ************************************
.                 *********Partisanship***************
.                 ************************************
. label def pi1 1 "Strong Democrat" 2 "Not Strong Dem" 3 "Lean Dem" 4 "Ind." 5 "Lean Rep" 6 "Not Strong Rep" 7 "Strong Rep"
{txt}
{com}. label def pi2 1 "Democrat" 2 "Republican" 3 "Independent"
{txt}
{com}. label def pi3 1 "Democrat" 0 "Republican"
{txt}
{com}. label def str 1 "Lean" 2 "Not Strong" 3 "Strong"
{txt}
{com}. label def part 1 "In-Partisan" 0 "Out-Partisan"
{txt}
{com}. 
.                 
. *W1
.         recode DER08W1 (0=1) (1=2) (2=3) (3=4) (4=5) (5=6) (6=7) (-7=.) (-6=.) (-5=.) (-4=.), gen(partyid1)
{txt}(4240 differences between DER08W1 and partyid1)

{com}.         label var partyid1 "PID (W1)"
{txt}
{com}.         label values partyid1 pi1
{txt}
{com}.         
.         gen pid_31 = .
{txt}(4240 missing values generated)

{com}.         replace pid_31 = 1 if partyid1 >=1 & partyid1 <= 3
{txt}(739 real changes made)

{com}.         replace pid_31 = 2 if partyid1 >=5 & partyid1 <= 7
{txt}(675 real changes made)

{com}.         replace pid_31 = 3 if partyid1 == 4
{txt}(200 real changes made)

{com}.         label var pid_31 "PID (W1)"
{txt}
{com}.         label values pid_31 pi2
{txt}
{com}.         
.         gen pid_21 = .
{txt}(4240 missing values generated)

{com}.         replace pid_21 = 1 if pid_31 == 1
{txt}(739 real changes made)

{com}.         replace pid_21 = 0 if pid_31 == 2
{txt}(675 real changes made)

{com}.         label var pid_21 "PID (W1)"
{txt}
{com}.         label values pid_21 pi3
{txt}
{com}.         
.         gen pid_str1 = .
{txt}(4240 missing values generated)

{com}.         replace pid_str1 =  1 if partyid == 3
{txt}(172 real changes made)

{com}.         replace pid_str1 =  1 if partyid == 5
{txt}(149 real changes made)

{com}.         replace pid_str1 =  2 if partyid == 2
{txt}(240 real changes made)

{com}.         replace pid_str1 =  2 if partyid == 6
{txt}(249 real changes made)

{com}.         replace pid_str1 =  3 if partyid == 1
{txt}(327 real changes made)

{com}.         replace pid_str1 =  3 if partyid == 7
{txt}(277 real changes made)

{com}.         label var pid_str1 "PID Str (W1)"
{txt}
{com}.         label values pid_str1 str
{txt}
{com}.         
.         tab partyid1

       {txt}PID (W1) {c |}      Freq.     Percent        Cum.
{hline 16}{c +}{hline 35}
Strong Democrat {c |}{res}        327       20.26       20.26
{txt} Not Strong Dem {c |}{res}        240       14.87       35.13
{txt}       Lean Dem {c |}{res}        172       10.66       45.79
{txt}           Ind. {c |}{res}        200       12.39       58.18
{txt}       Lean Rep {c |}{res}        149        9.23       67.41
{txt} Not Strong Rep {c |}{res}        249       15.43       82.84
{txt}     Strong Rep {c |}{res}        277       17.16      100.00
{txt}{hline 16}{c +}{hline 35}
          Total {c |}{res}      1,614      100.00
{txt}
{com}.         tab pid_31

   {txt}PID (W1) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
   Democrat {c |}{res}        739       45.79       45.79
{txt} Republican {c |}{res}        675       41.82       87.61
{txt}Independent {c |}{res}        200       12.39      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,614      100.00
{txt}
{com}.         tab pid_21

   {txt}PID (W1) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
 Republican {c |}{res}        675       47.74       47.74
{txt}   Democrat {c |}{res}        739       52.26      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,414      100.00
{txt}
{com}.         tab pid_str1

    {txt}PID Str {c |}
       (W1) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       Lean {c |}{res}        321       22.70       22.70
{txt} Not Strong {c |}{res}        489       34.58       57.28
{txt}     Strong {c |}{res}        604       42.72      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,414      100.00
{txt}
{com}.         
.         recode pid_21 (1=0) (0=1), gen(partisan_1)
{txt}(1414 differences between pid_21 and partisan_1)

{com}.         label var partisan_1 "Co-Partisan to Pres? (W1)"
{txt}
{com}.         label values partisan_1 part 
{txt}
{com}.         
.         
.         
. *W9
.         recode DER08W9 (0=1) (1=2) (2=3) (3=4) (4=5) (5=6) (6=7) (-7=.) (-6=.) (-5=.) (-4=.), gen(partyid9)
{txt}(4238 differences between DER08W9 and partyid9)

{com}.         label var partyid9 "PID (W9)"
{txt}
{com}.         label values partyid9 pi1
{txt}
{com}.         
.         gen pid_39 = .
{txt}(4240 missing values generated)

{com}.         replace pid_39 = 1 if partyid9 >=1 & partyid9 <= 3
{txt}(1257 real changes made)

{com}.         replace pid_39 = 2 if partyid9 >=5 & partyid9 <= 7
{txt}(1142 real changes made)

{com}.         replace pid_39 = 3 if partyid9 == 4
{txt}(334 real changes made)

{com}.         label var pid_39 "PID (W9)"
{txt}
{com}.         label values pid_39 pi2
{txt}
{com}.         
.         gen pid_29 = .
{txt}(4240 missing values generated)

{com}.         replace pid_29 = 1 if pid_39 == 1
{txt}(1257 real changes made)

{com}.         replace pid_29 = 0 if pid_39 == 2
{txt}(1142 real changes made)

{com}.         label var pid_29 "PID (W9)"
{txt}
{com}.         label values pid_29 pi3
{txt}
{com}.         
.         gen pid_str9 = .
{txt}(4240 missing values generated)

{com}.         replace pid_str9 =  1 if partyid9 == 3
{txt}(278 real changes made)

{com}.         replace pid_str9 =  1 if partyid9 == 5
{txt}(276 real changes made)

{com}.         replace pid_str9 =  2 if partyid9 == 2
{txt}(390 real changes made)

{com}.         replace pid_str9 =  2 if partyid9 == 6
{txt}(379 real changes made)

{com}.         replace pid_str9 =  3 if partyid9 == 1
{txt}(589 real changes made)

{com}.         replace pid_str9 =  3 if partyid9 == 7
{txt}(487 real changes made)

{com}.         label var pid_str9 "PID Str (W9)"
{txt}
{com}.         label values pid_str9 str
{txt}
{com}.         
.         tab partyid9

       {txt}PID (W9) {c |}      Freq.     Percent        Cum.
{hline 16}{c +}{hline 35}
Strong Democrat {c |}{res}        589       21.55       21.55
{txt} Not Strong Dem {c |}{res}        390       14.27       35.82
{txt}       Lean Dem {c |}{res}        278       10.17       45.99
{txt}           Ind. {c |}{res}        334       12.22       58.21
{txt}       Lean Rep {c |}{res}        276       10.10       68.31
{txt} Not Strong Rep {c |}{res}        379       13.87       82.18
{txt}     Strong Rep {c |}{res}        487       17.82      100.00
{txt}{hline 16}{c +}{hline 35}
          Total {c |}{res}      2,733      100.00
{txt}
{com}.         tab partyid9 DER08W9

                {txt}{c |}        der08w9. DERIVED. R party identification at wave 9
       PID (W9) {c |} 0. Strong  1. Not ve  2. Indepe  3. Indepe  4. Indepe  5. Not ve {c |}     Total
{hline 16}{c +}{hline 66}{c +}{hline 10}
Strong Democrat {c |}{res}       589          0          0          0          0          0 {txt}{c |}{res}       589 
{txt} Not Strong Dem {c |}{res}         0        390          0          0          0          0 {txt}{c |}{res}       390 
{txt}       Lean Dem {c |}{res}         0          0        278          0          0          0 {txt}{c |}{res}       278 
{txt}           Ind. {c |}{res}         0          0          0        334          0          0 {txt}{c |}{res}       334 
{txt}       Lean Rep {c |}{res}         0          0          0          0        276          0 {txt}{c |}{res}       276 
{txt} Not Strong Rep {c |}{res}         0          0          0          0          0        379 {txt}{c |}{res}       379 
{txt}     Strong Rep {c |}{res}         0          0          0          0          0          0 {txt}{c |}{res}       487 
{txt}{hline 16}{c +}{hline 66}{c +}{hline 10}
          Total {c |}{res}       589        390        278        334        276        379 {txt}{c |}{res}     2,733 


                {txt}{c |}  der08w9.
                {c |} DERIVED. R
                {c |}   party
                {c |} identifica
                {c |}  tion at
                {c |}   wave 9
       PID (W9) {c |} 6. Strong {c |}     Total
{hline 16}{c +}{hline 11}{c +}{hline 10}
Strong Democrat {c |}{res}         0 {txt}{c |}{res}       589 
{txt} Not Strong Dem {c |}{res}         0 {txt}{c |}{res}       390 
{txt}       Lean Dem {c |}{res}         0 {txt}{c |}{res}       278 
{txt}           Ind. {c |}{res}         0 {txt}{c |}{res}       334 
{txt}       Lean Rep {c |}{res}         0 {txt}{c |}{res}       276 
{txt} Not Strong Rep {c |}{res}         0 {txt}{c |}{res}       379 
{txt}     Strong Rep {c |}{res}       487 {txt}{c |}{res}       487 
{txt}{hline 16}{c +}{hline 11}{c +}{hline 10}
          Total {c |}{res}       487 {txt}{c |}{res}     2,733 

{txt}
{com}.         tab pid_39

   {txt}PID (W9) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
   Democrat {c |}{res}      1,257       45.99       45.99
{txt} Republican {c |}{res}      1,142       41.79       87.78
{txt}Independent {c |}{res}        334       12.22      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,733      100.00
{txt}
{com}.         tab pid_29

   {txt}PID (W9) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
 Republican {c |}{res}      1,142       47.60       47.60
{txt}   Democrat {c |}{res}      1,257       52.40      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,399      100.00
{txt}
{com}.         tab pid_str9

    {txt}PID Str {c |}
       (W9) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       Lean {c |}{res}        554       23.09       23.09
{txt} Not Strong {c |}{res}        769       32.06       55.15
{txt}     Strong {c |}{res}      1,076       44.85      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,399      100.00
{txt}
{com}.         
.         recode pid_29 (1=0) (0=1), gen(partisan_9)
{txt}(2399 differences between pid_29 and partisan_9)

{com}.         label var partisan_9 "Co-Partisan to Pres? (W1)"
{txt}
{com}.         label values partisan_9 part 
{txt}
{com}.         
.         gen partisan_9rev = pid_29
{txt}(1841 missing values generated)

{com}.         label var partisan_9rev "Co-Paritsan to Pres Obama? (W9)"
{txt}
{com}.         label values partisan_9rev part
{txt}
{com}.         
.         
. *W10
.         recode DER08W10 (0=1) (1=2) (2=3) (3=4) (4=5) (5=6) (6=7) (-7=.) (-6=.) (-5=.) (-4=.), gen(partyid10)
{txt}(4240 differences between DER08W10 and partyid10)

{com}.         label var partyid10 "PID (W10)"
{txt}
{com}.         label values partyid10 pi1
{txt}
{com}.         
.         gen pid_310 = .
{txt}(4240 missing values generated)

{com}.         replace pid_310 = 1 if partyid10 >=1 & partyid10 <= 3
{txt}(1259 real changes made)

{com}.         replace pid_310 = 2 if partyid10 >=5 & partyid10 <= 7
{txt}(1102 real changes made)

{com}.         replace pid_310 = 3 if partyid10 == 4
{txt}(330 real changes made)

{com}.         label var pid_310 "PID (W10)"
{txt}
{com}.         label values pid_310 pi2
{txt}
{com}.         
.         gen pid_210 = .
{txt}(4240 missing values generated)

{com}.         replace pid_210 = 1 if pid_310 == 1
{txt}(1259 real changes made)

{com}.         replace pid_210 = 0 if pid_310 == 2
{txt}(1102 real changes made)

{com}.         label var pid_210 "PID (W10)"
{txt}
{com}.         label values pid_210 pi3
{txt}
{com}.         
.         gen pid_str10 = .
{txt}(4240 missing values generated)

{com}.         replace pid_str10 =  1 if partyid10 == 3
{txt}(257 real changes made)

{com}.         replace pid_str10 =  1 if partyid10 == 5
{txt}(252 real changes made)

{com}.         replace pid_str10 =  2 if partyid10 == 2
{txt}(405 real changes made)

{com}.         replace pid_str10 =  2 if partyid10 == 6
{txt}(412 real changes made)

{com}.         replace pid_str10 =  3 if partyid10 == 1
{txt}(597 real changes made)

{com}.         replace pid_str10 =  3 if partyid10 == 7
{txt}(438 real changes made)

{com}.         label var pid_str10 "PID Str (W10)"
{txt}
{com}.         label values pid_str10 str
{txt}
{com}.         
.         tab partyid10

      {txt}PID (W10) {c |}      Freq.     Percent        Cum.
{hline 16}{c +}{hline 35}
Strong Democrat {c |}{res}        597       22.19       22.19
{txt} Not Strong Dem {c |}{res}        405       15.05       37.24
{txt}       Lean Dem {c |}{res}        257        9.55       46.79
{txt}           Ind. {c |}{res}        330       12.26       59.05
{txt}       Lean Rep {c |}{res}        252        9.36       68.41
{txt} Not Strong Rep {c |}{res}        412       15.31       83.72
{txt}     Strong Rep {c |}{res}        438       16.28      100.00
{txt}{hline 16}{c +}{hline 35}
          Total {c |}{res}      2,691      100.00
{txt}
{com}.         tab partyid10 DER08W10

                {txt}{c |}       der08w10. DERIVED. R party identification at wave 10
      PID (W10) {c |} 0. Strong  1. Not ve  2. Indepe  3. Indepe  4. Indepe  5. Not ve {c |}     Total
{hline 16}{c +}{hline 66}{c +}{hline 10}
Strong Democrat {c |}{res}       597          0          0          0          0          0 {txt}{c |}{res}       597 
{txt} Not Strong Dem {c |}{res}         0        405          0          0          0          0 {txt}{c |}{res}       405 
{txt}       Lean Dem {c |}{res}         0          0        257          0          0          0 {txt}{c |}{res}       257 
{txt}           Ind. {c |}{res}         0          0          0        330          0          0 {txt}{c |}{res}       330 
{txt}       Lean Rep {c |}{res}         0          0          0          0        252          0 {txt}{c |}{res}       252 
{txt} Not Strong Rep {c |}{res}         0          0          0          0          0        412 {txt}{c |}{res}       412 
{txt}     Strong Rep {c |}{res}         0          0          0          0          0          0 {txt}{c |}{res}       438 
{txt}{hline 16}{c +}{hline 66}{c +}{hline 10}
          Total {c |}{res}       597        405        257        330        252        412 {txt}{c |}{res}     2,691 


                {txt}{c |} der08w10.
                {c |} DERIVED. R
                {c |}   party
                {c |} identifica
                {c |}  tion at
                {c |}  wave 10
      PID (W10) {c |} 6. Strong {c |}     Total
{hline 16}{c +}{hline 11}{c +}{hline 10}
Strong Democrat {c |}{res}         0 {txt}{c |}{res}       597 
{txt} Not Strong Dem {c |}{res}         0 {txt}{c |}{res}       405 
{txt}       Lean Dem {c |}{res}         0 {txt}{c |}{res}       257 
{txt}           Ind. {c |}{res}         0 {txt}{c |}{res}       330 
{txt}       Lean Rep {c |}{res}         0 {txt}{c |}{res}       252 
{txt} Not Strong Rep {c |}{res}         0 {txt}{c |}{res}       412 
{txt}     Strong Rep {c |}{res}       438 {txt}{c |}{res}       438 
{txt}{hline 16}{c +}{hline 11}{c +}{hline 10}
          Total {c |}{res}       438 {txt}{c |}{res}     2,691 

{txt}
{com}.         tab pid_310

  {txt}PID (W10) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
   Democrat {c |}{res}      1,259       46.79       46.79
{txt} Republican {c |}{res}      1,102       40.95       87.74
{txt}Independent {c |}{res}        330       12.26      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,691      100.00
{txt}
{com}.         tab pid_210

  {txt}PID (W10) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
 Republican {c |}{res}      1,102       46.68       46.68
{txt}   Democrat {c |}{res}      1,259       53.32      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,361      100.00
{txt}
{com}.         tab pid_str10

    {txt}PID Str {c |}
      (W10) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       Lean {c |}{res}        509       21.56       21.56
{txt} Not Strong {c |}{res}        817       34.60       56.16
{txt}     Strong {c |}{res}      1,035       43.84      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,361      100.00
{txt}
{com}.         
.         recode partyid10 (1=4) (2=3) (3=2) (4=1) (5=2) (6=3) (7=4), gen(pid_str10_full)
{txt}(2691 differences between partyid10 and pid_str10_full)

{com}.         label var pid_str10 "PID Str (W10)"
{txt}
{com}.         label def full 1 "Ind" 2 "Lean" 3 "Not Strong" 4 "Strong"
{txt}
{com}.         label values pid_str10 full
{txt}
{com}.         
.         
.         
.         
. *W11
.         recode DER08W11 (0=1) (1=2) (2=3) (3=4) (4=5) (5=6) (6=7) (-7=.) (-6=.) (-5=.) (-4=.), gen(partyid11)
{txt}(4240 differences between DER08W11 and partyid11)

{com}.         label var partyid11 "PID (W11)"
{txt}
{com}.         label values partyid11 pi1
{txt}
{com}.         
.         gen pid_311 = .
{txt}(4240 missing values generated)

{com}.         replace pid_311 = 1 if partyid11 >=1 & partyid11 <= 3
{txt}(1281 real changes made)

{com}.         replace pid_311 = 2 if partyid11 >=5 & partyid11 <= 7
{txt}(1123 real changes made)

{com}.         replace pid_311 = 3 if partyid11 == 4
{txt}(260 real changes made)

{com}.         label var pid_311 "PID (W11)"
{txt}
{com}.         label values pid_311 pi2
{txt}
{com}.         
.         gen pid_211 = .
{txt}(4240 missing values generated)

{com}.         replace pid_211 = 1 if pid_311 == 1
{txt}(1281 real changes made)

{com}.         replace pid_211 = 0 if pid_311 == 2
{txt}(1123 real changes made)

{com}.         label var pid_211 "PID (W11)"
{txt}
{com}.         label values pid_211 pi3
{txt}
{com}.         
.         gen pid_str11 = .
{txt}(4240 missing values generated)

{com}.         replace pid_str11 =  1 if partyid11 == 3
{txt}(243 real changes made)

{com}.         replace pid_str11 =  1 if partyid11 == 5
{txt}(222 real changes made)

{com}.         replace pid_str11 =  2 if partyid11 == 2
{txt}(353 real changes made)

{com}.         replace pid_str11 =  2 if partyid11 == 6
{txt}(355 real changes made)

{com}.         replace pid_str11 =  3 if partyid11 == 1
{txt}(685 real changes made)

{com}.         replace pid_str11 =  3 if partyid11 == 7
{txt}(546 real changes made)

{com}.         label var pid_str11 "PID Str (W9)"
{txt}
{com}.         label values pid_str11 str
{txt}
{com}.         
.         tab partyid11

      {txt}PID (W11) {c |}      Freq.     Percent        Cum.
{hline 16}{c +}{hline 35}
Strong Democrat {c |}{res}        685       25.71       25.71
{txt} Not Strong Dem {c |}{res}        353       13.25       38.96
{txt}       Lean Dem {c |}{res}        243        9.12       48.09
{txt}           Ind. {c |}{res}        260        9.76       57.85
{txt}       Lean Rep {c |}{res}        222        8.33       66.18
{txt} Not Strong Rep {c |}{res}        355       13.33       79.50
{txt}     Strong Rep {c |}{res}        546       20.50      100.00
{txt}{hline 16}{c +}{hline 35}
          Total {c |}{res}      2,664      100.00
{txt}
{com}.         tab partyid11 DER08W11

                {txt}{c |}       der08w11. DERIVED. R party identification at wave 11
      PID (W11) {c |} 0. Strong  1. Not ve  2. Indepe  3. Indepe  4. Indepe  5. Not ve {c |}     Total
{hline 16}{c +}{hline 66}{c +}{hline 10}
Strong Democrat {c |}{res}       685          0          0          0          0          0 {txt}{c |}{res}       685 
{txt} Not Strong Dem {c |}{res}         0        353          0          0          0          0 {txt}{c |}{res}       353 
{txt}       Lean Dem {c |}{res}         0          0        243          0          0          0 {txt}{c |}{res}       243 
{txt}           Ind. {c |}{res}         0          0          0        260          0          0 {txt}{c |}{res}       260 
{txt}       Lean Rep {c |}{res}         0          0          0          0        222          0 {txt}{c |}{res}       222 
{txt} Not Strong Rep {c |}{res}         0          0          0          0          0        355 {txt}{c |}{res}       355 
{txt}     Strong Rep {c |}{res}         0          0          0          0          0          0 {txt}{c |}{res}       546 
{txt}{hline 16}{c +}{hline 66}{c +}{hline 10}
          Total {c |}{res}       685        353        243        260        222        355 {txt}{c |}{res}     2,664 


                {txt}{c |} der08w11.
                {c |} DERIVED. R
                {c |}   party
                {c |} identifica
                {c |}  tion at
                {c |}  wave 11
      PID (W11) {c |} 6. Strong {c |}     Total
{hline 16}{c +}{hline 11}{c +}{hline 10}
Strong Democrat {c |}{res}         0 {txt}{c |}{res}       685 
{txt} Not Strong Dem {c |}{res}         0 {txt}{c |}{res}       353 
{txt}       Lean Dem {c |}{res}         0 {txt}{c |}{res}       243 
{txt}           Ind. {c |}{res}         0 {txt}{c |}{res}       260 
{txt}       Lean Rep {c |}{res}         0 {txt}{c |}{res}       222 
{txt} Not Strong Rep {c |}{res}         0 {txt}{c |}{res}       355 
{txt}     Strong Rep {c |}{res}       546 {txt}{c |}{res}       546 
{txt}{hline 16}{c +}{hline 11}{c +}{hline 10}
          Total {c |}{res}       546 {txt}{c |}{res}     2,664 

{txt}
{com}.         tab pid_311

  {txt}PID (W11) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
   Democrat {c |}{res}      1,281       48.09       48.09
{txt} Republican {c |}{res}      1,123       42.15       90.24
{txt}Independent {c |}{res}        260        9.76      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,664      100.00
{txt}
{com}.         tab pid_211

  {txt}PID (W11) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
 Republican {c |}{res}      1,123       46.71       46.71
{txt}   Democrat {c |}{res}      1,281       53.29      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,404      100.00
{txt}
{com}.         tab pid_str11

    {txt}PID Str {c |}
       (W9) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       Lean {c |}{res}        465       19.34       19.34
{txt} Not Strong {c |}{res}        708       29.45       48.79
{txt}     Strong {c |}{res}      1,231       51.21      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,404      100.00
{txt}
{com}. 
.         recode partyid11 (1=4) (2=3) (3=2) (4=1) (5=2) (6=3) (7=4), gen(pid_str11_full)
{txt}(2664 differences between partyid11 and pid_str11_full)

{com}.         label var pid_str11 "PID Str (W11)"
{txt}
{com}.         label values pid_str11 full
{txt}
{com}.         
.         
.         
. *W17
.         recode DER08W17 (0=1) (1=2) (2=3) (3=4) (4=5) (5=6) (6=7) ///
>                 (-7=.) (-6=.) (-5=.) (-4=.) (-2=.), gen(partyid17)
{txt}(4240 differences between DER08W17 and partyid17)

{com}.         label var partyid17 "PID (W17)"
{txt}
{com}.         label values partyid17 pi1
{txt}
{com}.         
.         gen pid_317 = .
{txt}(4240 missing values generated)

{com}.         replace pid_317 = 1 if partyid17 >=1 & partyid17 <= 3
{txt}(1133 real changes made)

{com}.         replace pid_317 = 2 if partyid17 >=5 & partyid17 <= 7
{txt}(994 real changes made)

{com}.         replace pid_317 = 3 if partyid17 == 4
{txt}(263 real changes made)

{com}.         label var pid_317 "PID (W17)"
{txt}
{com}.         label values pid_317 pi2
{txt}
{com}.         
.         gen pid_217 = .
{txt}(4240 missing values generated)

{com}.         replace pid_217 = 1 if pid_317 == 1
{txt}(1133 real changes made)

{com}.         replace pid_217 = 0 if pid_317 == 2
{txt}(994 real changes made)

{com}.         label var pid_217 "PID (W17)"
{txt}
{com}.         label values pid_217 pi3
{txt}
{com}.         
.         gen pid_str17 = .
{txt}(4240 missing values generated)

{com}.         replace pid_str17 =  1 if partyid17 == 3
{txt}(216 real changes made)

{com}.         replace pid_str17 =  1 if partyid17 == 5
{txt}(232 real changes made)

{com}.         replace pid_str17 =  2 if partyid17 == 2
{txt}(357 real changes made)

{com}.         replace pid_str17 =  2 if partyid17 == 6
{txt}(378 real changes made)

{com}.         replace pid_str17 =  3 if partyid17 == 1
{txt}(560 real changes made)

{com}.         replace pid_str17 =  3 if partyid17 == 7
{txt}(384 real changes made)

{com}.         label var pid_str17 "PID Str (W9)"
{txt}
{com}.         label values pid_str17 str
{txt}
{com}.         
.         tab partyid17

      {txt}PID (W17) {c |}      Freq.     Percent        Cum.
{hline 16}{c +}{hline 35}
Strong Democrat {c |}{res}        560       23.43       23.43
{txt} Not Strong Dem {c |}{res}        357       14.94       38.37
{txt}       Lean Dem {c |}{res}        216        9.04       47.41
{txt}           Ind. {c |}{res}        263       11.00       58.41
{txt}       Lean Rep {c |}{res}        232        9.71       68.12
{txt} Not Strong Rep {c |}{res}        378       15.82       83.93
{txt}     Strong Rep {c |}{res}        384       16.07      100.00
{txt}{hline 16}{c +}{hline 35}
          Total {c |}{res}      2,390      100.00
{txt}
{com}.         tab partyid17 DER08W17

                {txt}{c |}       der08w17. DERIVED. R party identification at wave 17
      PID (W17) {c |} 0. Strong  1. Not ve  2. Indepe  3. Indepe  4. Indepe  5. Not ve {c |}     Total
{hline 16}{c +}{hline 66}{c +}{hline 10}
Strong Democrat {c |}{res}       560          0          0          0          0          0 {txt}{c |}{res}       560 
{txt} Not Strong Dem {c |}{res}         0        357          0          0          0          0 {txt}{c |}{res}       357 
{txt}       Lean Dem {c |}{res}         0          0        216          0          0          0 {txt}{c |}{res}       216 
{txt}           Ind. {c |}{res}         0          0          0        263          0          0 {txt}{c |}{res}       263 
{txt}       Lean Rep {c |}{res}         0          0          0          0        232          0 {txt}{c |}{res}       232 
{txt} Not Strong Rep {c |}{res}         0          0          0          0          0        378 {txt}{c |}{res}       378 
{txt}     Strong Rep {c |}{res}         0          0          0          0          0          0 {txt}{c |}{res}       384 
{txt}{hline 16}{c +}{hline 66}{c +}{hline 10}
          Total {c |}{res}       560        357        216        263        232        378 {txt}{c |}{res}     2,390 


                {txt}{c |} der08w17.
                {c |} DERIVED. R
                {c |}   party
                {c |} identifica
                {c |}  tion at
                {c |}  wave 17
      PID (W17) {c |} 6. Strong {c |}     Total
{hline 16}{c +}{hline 11}{c +}{hline 10}
Strong Democrat {c |}{res}         0 {txt}{c |}{res}       560 
{txt} Not Strong Dem {c |}{res}         0 {txt}{c |}{res}       357 
{txt}       Lean Dem {c |}{res}         0 {txt}{c |}{res}       216 
{txt}           Ind. {c |}{res}         0 {txt}{c |}{res}       263 
{txt}       Lean Rep {c |}{res}         0 {txt}{c |}{res}       232 
{txt} Not Strong Rep {c |}{res}         0 {txt}{c |}{res}       378 
{txt}     Strong Rep {c |}{res}       384 {txt}{c |}{res}       384 
{txt}{hline 16}{c +}{hline 11}{c +}{hline 10}
          Total {c |}{res}       384 {txt}{c |}{res}     2,390 

{txt}
{com}.         tab pid_317

  {txt}PID (W17) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
   Democrat {c |}{res}      1,133       47.41       47.41
{txt} Republican {c |}{res}        994       41.59       89.00
{txt}Independent {c |}{res}        263       11.00      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,390      100.00
{txt}
{com}.         tab pid_217

  {txt}PID (W17) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
 Republican {c |}{res}        994       46.73       46.73
{txt}   Democrat {c |}{res}      1,133       53.27      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,127      100.00
{txt}
{com}.         tab pid_str17

    {txt}PID Str {c |}
       (W9) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       Lean {c |}{res}        448       21.06       21.06
{txt} Not Strong {c |}{res}        735       34.56       55.62
{txt}     Strong {c |}{res}        944       44.38      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,127      100.00
{txt}
{com}.         
.         gen partisan_17 = pid_217
{txt}(2113 missing values generated)

{com}.         label var partisan_17 "Co-Partisan to Pres Obama? (W17)"
{txt}
{com}.         label values partisan_17 part
{txt}
{com}.         
.         recode partyid17 (1=4) (2=3) (3=2) (4=1) (5=2) (6=3) (7=4), gen(pid_str17_full)
{txt}(2390 differences between partyid17 and pid_str17_full)

{com}.         label var pid_str17 "PID Str (W10)"
{txt}
{com}.         label values pid_str17 full
{txt}
{com}.         
.                 
. *W19
.         recode DER08W19 (0=1) (1=2) (2=3) (3=4) (4=5) (5=6) (6=7) (-7=.) (-6=.) (-5=.) (-4=.) (-2=.), gen(partyid19)
{txt}(4240 differences between DER08W19 and partyid19)

{com}.         label var partyid19 "PID (W19)"
{txt}
{com}.         label values partyid19 pi1
{txt}
{com}.         
.         gen pid_319 = .
{txt}(4240 missing values generated)

{com}.         replace pid_319 = 1 if partyid19 >=1 & partyid19 <= 3
{txt}(1141 real changes made)

{com}.         replace pid_319 = 2 if partyid19 >=5 & partyid19 <= 7
{txt}(983 real changes made)

{com}.         replace pid_319 = 3 if partyid19 == 4
{txt}(263 real changes made)

{com}.         label var pid_319 "PID (W19)"
{txt}
{com}.         label values pid_319 pi2
{txt}
{com}.         
.         gen pid_219 = .
{txt}(4240 missing values generated)

{com}.         replace pid_219 = 1 if pid_319 == 1
{txt}(1141 real changes made)

{com}.         replace pid_219 = 0 if pid_319 == 2
{txt}(983 real changes made)

{com}.         label var pid_219 "PID (W19)"
{txt}
{com}.         label values pid_219 pi3
{txt}
{com}.         
.         gen pid_str19 = .
{txt}(4240 missing values generated)

{com}.         replace pid_str19 =  1 if partyid19 == 3
{txt}(252 real changes made)

{com}.         replace pid_str19 =  1 if partyid19 == 5
{txt}(246 real changes made)

{com}.         replace pid_str19 =  2 if partyid19 == 2
{txt}(368 real changes made)

{com}.         replace pid_str19 =  2 if partyid19 == 6
{txt}(383 real changes made)

{com}.         replace pid_str19 =  3 if partyid19 == 1
{txt}(521 real changes made)

{com}.         replace pid_str19 =  3 if partyid19 == 7
{txt}(354 real changes made)

{com}.         label var pid_str19 "PID Str (W9)"
{txt}
{com}.         label values pid_str19 str
{txt}
{com}.         
.         tab partyid19

      {txt}PID (W19) {c |}      Freq.     Percent        Cum.
{hline 16}{c +}{hline 35}
Strong Democrat {c |}{res}        521       21.83       21.83
{txt} Not Strong Dem {c |}{res}        368       15.42       37.24
{txt}       Lean Dem {c |}{res}        252       10.56       47.80
{txt}           Ind. {c |}{res}        263       11.02       58.82
{txt}       Lean Rep {c |}{res}        246       10.31       69.12
{txt} Not Strong Rep {c |}{res}        383       16.05       85.17
{txt}     Strong Rep {c |}{res}        354       14.83      100.00
{txt}{hline 16}{c +}{hline 35}
          Total {c |}{res}      2,387      100.00
{txt}
{com}.         tab partyid19 DER08W19

                {txt}{c |}       der08w19. DERIVED. R party identification at wave 19
      PID (W19) {c |} 0. Strong  1. Not ve  2. Indepe  3. Indepe  4. Indepe  5. Not ve {c |}     Total
{hline 16}{c +}{hline 66}{c +}{hline 10}
Strong Democrat {c |}{res}       521          0          0          0          0          0 {txt}{c |}{res}       521 
{txt} Not Strong Dem {c |}{res}         0        368          0          0          0          0 {txt}{c |}{res}       368 
{txt}       Lean Dem {c |}{res}         0          0        252          0          0          0 {txt}{c |}{res}       252 
{txt}           Ind. {c |}{res}         0          0          0        263          0          0 {txt}{c |}{res}       263 
{txt}       Lean Rep {c |}{res}         0          0          0          0        246          0 {txt}{c |}{res}       246 
{txt} Not Strong Rep {c |}{res}         0          0          0          0          0        383 {txt}{c |}{res}       383 
{txt}     Strong Rep {c |}{res}         0          0          0          0          0          0 {txt}{c |}{res}       354 
{txt}{hline 16}{c +}{hline 66}{c +}{hline 10}
          Total {c |}{res}       521        368        252        263        246        383 {txt}{c |}{res}     2,387 


                {txt}{c |} der08w19.
                {c |} DERIVED. R
                {c |}   party
                {c |} identifica
                {c |}  tion at
                {c |}  wave 19
      PID (W19) {c |} 6. Strong {c |}     Total
{hline 16}{c +}{hline 11}{c +}{hline 10}
Strong Democrat {c |}{res}         0 {txt}{c |}{res}       521 
{txt} Not Strong Dem {c |}{res}         0 {txt}{c |}{res}       368 
{txt}       Lean Dem {c |}{res}         0 {txt}{c |}{res}       252 
{txt}           Ind. {c |}{res}         0 {txt}{c |}{res}       263 
{txt}       Lean Rep {c |}{res}         0 {txt}{c |}{res}       246 
{txt} Not Strong Rep {c |}{res}         0 {txt}{c |}{res}       383 
{txt}     Strong Rep {c |}{res}       354 {txt}{c |}{res}       354 
{txt}{hline 16}{c +}{hline 11}{c +}{hline 10}
          Total {c |}{res}       354 {txt}{c |}{res}     2,387 

{txt}
{com}.         tab pid_319

  {txt}PID (W19) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
   Democrat {c |}{res}      1,141       47.80       47.80
{txt} Republican {c |}{res}        983       41.18       88.98
{txt}Independent {c |}{res}        263       11.02      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,387      100.00
{txt}
{com}.         tab pid_219

  {txt}PID (W19) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
 Republican {c |}{res}        983       46.28       46.28
{txt}   Democrat {c |}{res}      1,141       53.72      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,124      100.00
{txt}
{com}.         tab pid_str19

    {txt}PID Str {c |}
       (W9) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       Lean {c |}{res}        498       23.45       23.45
{txt} Not Strong {c |}{res}        751       35.36       58.80
{txt}     Strong {c |}{res}        875       41.20      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,124      100.00
{txt}
{com}.         
.         recode partyid19 (1=4) (2=3) (3=2) (4=1) (5=2) (6=3) (7=4), gen(pid_str19_full)
{txt}(2387 differences between partyid19 and pid_str19_full)

{com}.         label var pid_str19 "PID Str (W10)"
{txt}
{com}.         label values pid_str19 full
{txt}
{com}.         
.                 
. *Relationship over time
.         pwcorr partyid1 partyid9 partyid10 partyid11 partyid17 partyid19, sig

             {txt}{c |} partyid1 partyid9 party~10 party~11 party~17 party~19
{hline 13}{c +}{hline 54}
    partyid1 {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
    partyid9 {c |} {res}  0.8944   1.0000 
             {txt}{c |}{res}   0.0000
             {txt}{c |}
   partyid10 {c |} {res}  0.8997   0.9207   1.0000 
             {txt}{c |}{res}   0.0000   0.0000
             {txt}{c |}
   partyid11 {c |} {res}  0.8927   0.9173   0.9315   1.0000 
             {txt}{c |}{res}   0.0000   0.0000   0.0000
             {txt}{c |}
   partyid17 {c |} {res}  0.8964   0.9124   0.9221   0.9245   1.0000 
             {txt}{c |}{res}   0.0000   0.0000   0.0000   0.0000
             {txt}{c |}
   partyid19 {c |} {res}  0.8911   0.9090   0.9165   0.9183   0.9370   1.0000 
             {txt}{c |}{res}   0.0000   0.0000   0.0000   0.0000   0.0000
             {txt}{c |}

{com}.         pwcorr pid_str1 pid_str9 pid_str10 pid_str11 pid_str17 pid_str19, sig

             {txt}{c |} pid_str1 pid_str9 pid_s~10 pid_s~11 pid_s~17 pid_s~19
{hline 13}{c +}{hline 54}
    pid_str1 {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
    pid_str9 {c |} {res}  0.6608   1.0000 
             {txt}{c |}{res}   0.0000
             {txt}{c |}
   pid_str10 {c |} {res}  0.6228   0.7394   1.0000 
             {txt}{c |}{res}   0.0000   0.0000
             {txt}{c |}
   pid_str11 {c |} {res}  0.6404   0.7292   0.7538   1.0000 
             {txt}{c |}{res}   0.0000   0.0000   0.0000
             {txt}{c |}
   pid_str17 {c |} {res}  0.6375   0.6689   0.7117   0.6967   1.0000 
             {txt}{c |}{res}   0.0000   0.0000   0.0000   0.0000
             {txt}{c |}
   pid_str19 {c |} {res}  0.6007   0.6746   0.7121   0.6880   0.7483   1.0000 
             {txt}{c |}{res}   0.0000   0.0000   0.0000   0.0000   0.0000
             {txt}{c |}

{com}.                 
.                 
.         tab w1_retro pid_21, row col chi2
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

      Econ {c |}
    Better {c |}
  than 1yr {c |}       PID (W1)
 Ago (W1)? {c |} Republica   Democrat {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
     Worse {c |}{res}       429        621 {txt}{c |}{res}     1,050 
           {txt}{c |}{res}     40.86      59.14 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     63.56      84.03 {txt}{c |}{res}     74.26 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
      Same {c |}{res}       209        108 {txt}{c |}{res}       317 
           {txt}{c |}{res}     65.93      34.07 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     30.96      14.61 {txt}{c |}{res}     22.42 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
    Better {c |}{res}        37         10 {txt}{c |}{res}        47 
           {txt}{c |}{res}     78.72      21.28 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}      5.48       1.35 {txt}{c |}{res}      3.32 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       675        739 {txt}{c |}{res}     1,414 
           {txt}{c |}{res}     47.74      52.26 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}    100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}2{txt}) = {res} 80.0663  {txt} Pr = {res}0.000
{txt}
{com}.         tab w6_retro pid_21, row col chi2
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

      Econ {c |}
    Better {c |}
  than 1yr {c |}       PID (W1)
 Ago (W6)? {c |} Republica   Democrat {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
     Worse {c |}{res}       458        561 {txt}{c |}{res}     1,019 
           {txt}{c |}{res}     44.95      55.05 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     79.10      91.22 {txt}{c |}{res}     85.34 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
      Same {c |}{res}       108         50 {txt}{c |}{res}       158 
           {txt}{c |}{res}     68.35      31.65 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     18.65       8.13 {txt}{c |}{res}     13.23 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
    Better {c |}{res}        13          4 {txt}{c |}{res}        17 
           {txt}{c |}{res}     76.47      23.53 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}      2.25       0.65 {txt}{c |}{res}      1.42 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       579        615 {txt}{c |}{res}     1,194 
           {txt}{c |}{res}     48.49      51.51 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}    100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}2{txt}) = {res} 35.4138  {txt} Pr = {res}0.000
{txt}
{com}.         tab w10_retro pid_21, row col chi2
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

      Econ {c |}
    Better {c |}
  than 1yr {c |}       PID (W1)
Ago (W10)? {c |} Republica   Democrat {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
     Worse {c |}{res}       535        596 {txt}{c |}{res}     1,131 
           {txt}{c |}{res}     47.30      52.70 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     90.52      96.75 {txt}{c |}{res}     93.70 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
      Same {c |}{res}        53         19 {txt}{c |}{res}        72 
           {txt}{c |}{res}     73.61      26.39 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}      8.97       3.08 {txt}{c |}{res}      5.97 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
    Better {c |}{res}         3          1 {txt}{c |}{res}         4 
           {txt}{c |}{res}     75.00      25.00 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}      0.51       0.16 {txt}{c |}{res}      0.33 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       591        616 {txt}{c |}{res}     1,207 
           {txt}{c |}{res}     48.96      51.04 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}    100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}2{txt}) = {res} 19.8363  {txt} Pr = {res}0.000
{txt}
{com}.         tab w11_retro pid_21, row col chi2
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

Econ Worse {c |}
  than 1yr {c |}       PID (W1)
Ago (W11)? {c |} Republica   Democrat {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
     Worse {c |}{res}       536        593 {txt}{c |}{res}     1,129 
           {txt}{c |}{res}     47.48      52.52 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     90.08      96.42 {txt}{c |}{res}     93.31 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
      Same {c |}{res}        55         21 {txt}{c |}{res}        76 
           {txt}{c |}{res}     72.37      27.63 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}      9.24       3.41 {txt}{c |}{res}      6.28 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
    Better {c |}{res}         4          1 {txt}{c |}{res}         5 
           {txt}{c |}{res}     80.00      20.00 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}      0.67       0.16 {txt}{c |}{res}      0.41 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       595        615 {txt}{c |}{res}     1,210 
           {txt}{c |}{res}     49.17      50.83 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}    100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}2{txt}) = {res} 19.5631  {txt} Pr = {res}0.000
{txt}
{com}.         tab w10_retro pid_29, row col chi2
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

      Econ {c |}
    Better {c |}
  than 1yr {c |}       PID (W9)
Ago (W10)? {c |} Republica   Democrat {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
     Worse {c |}{res}       935      1,104 {txt}{c |}{res}     2,039 
           {txt}{c |}{res}     45.86      54.14 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     90.86      97.53 {txt}{c |}{res}     94.35 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
      Same {c |}{res}        90         23 {txt}{c |}{res}       113 
           {txt}{c |}{res}     79.65      20.35 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}      8.75       2.03 {txt}{c |}{res}      5.23 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
    Better {c |}{res}         4          5 {txt}{c |}{res}         9 
           {txt}{c |}{res}     44.44      55.56 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}      0.39       0.44 {txt}{c |}{res}      0.42 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}     1,029      1,132 {txt}{c |}{res}     2,161 
           {txt}{c |}{res}     47.62      52.38 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}    100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}2{txt}) = {res} 49.0463  {txt} Pr = {res}0.000
{txt}
{com}.         tab w11_retro pid_29, row col chi2
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

Econ Worse {c |}
  than 1yr {c |}       PID (W9)
Ago (W11)? {c |} Republica   Democrat {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
     Worse {c |}{res}       951      1,085 {txt}{c |}{res}     2,036 
           {txt}{c |}{res}     46.71      53.29 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     90.83      96.19 {txt}{c |}{res}     93.61 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
      Same {c |}{res}        90         38 {txt}{c |}{res}       128 
           {txt}{c |}{res}     70.31      29.69 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}      8.60       3.37 {txt}{c |}{res}      5.89 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
    Better {c |}{res}         6          5 {txt}{c |}{res}        11 
           {txt}{c |}{res}     54.55      45.45 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}      0.57       0.44 {txt}{c |}{res}      0.51 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}     1,047      1,128 {txt}{c |}{res}     2,175 
           {txt}{c |}{res}     48.14      51.86 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}    100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}2{txt}) = {res} 27.0561  {txt} Pr = {res}0.000
{txt}
{com}.         
.         tab w17_retro pid_21, row col chi2
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

Econ Worse {c |}
 than Jan. {c |}
      2009 {c |}       PID (W1)
    (W17)? {c |} Republica   Democrat {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
     Worse {c |}{res}       331        258 {txt}{c |}{res}       589 
           {txt}{c |}{res}     56.20      43.80 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     61.52      45.74 {txt}{c |}{res}     53.45 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
      Same {c |}{res}       145        197 {txt}{c |}{res}       342 
           {txt}{c |}{res}     42.40      57.60 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     26.95      34.93 {txt}{c |}{res}     31.03 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
    Better {c |}{res}        62        109 {txt}{c |}{res}       171 
           {txt}{c |}{res}     36.26      63.74 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     11.52      19.33 {txt}{c |}{res}     15.52 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       538        564 {txt}{c |}{res}     1,102 
           {txt}{c |}{res}     48.82      51.18 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}    100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}2{txt}) = {res} 29.2750  {txt} Pr = {res}0.000
{txt}
{com}.         tab w17_retro pid_29, row col chi2
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

Econ Worse {c |}
 than Jan. {c |}
      2009 {c |}       PID (W9)
    (W17)? {c |} Republica   Democrat {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
     Worse {c |}{res}       587        431 {txt}{c |}{res}     1,018 
           {txt}{c |}{res}     57.66      42.34 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     62.71      42.89 {txt}{c |}{res}     52.45 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
      Same {c |}{res}       252        364 {txt}{c |}{res}       616 
           {txt}{c |}{res}     40.91      59.09 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     26.92      36.22 {txt}{c |}{res}     31.74 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
    Better {c |}{res}        97        210 {txt}{c |}{res}       307 
           {txt}{c |}{res}     31.60      68.40 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     10.36      20.90 {txt}{c |}{res}     15.82 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       936      1,005 {txt}{c |}{res}     1,941 
           {txt}{c |}{res}     48.22      51.78 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}    100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}2{txt}) = {res} 83.5148  {txt} Pr = {res}0.000
{txt}
{com}.         tab w19_retro pid_21, row col chi2
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

Econ Worse {c |}
 than Jan. {c |}
      2009 {c |}       PID (W1)
    (W19)? {c |} Republica   Democrat {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
     Worse {c |}{res}       408        237 {txt}{c |}{res}       645 
           {txt}{c |}{res}     63.26      36.74 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     75.98      42.32 {txt}{c |}{res}     58.80 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
      Same {c |}{res}        98        224 {txt}{c |}{res}       322 
           {txt}{c |}{res}     30.43      69.57 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     18.25      40.00 {txt}{c |}{res}     29.35 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
    Better {c |}{res}        31         99 {txt}{c |}{res}       130 
           {txt}{c |}{res}     23.85      76.15 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}      5.77      17.68 {txt}{c |}{res}     11.85 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       537        560 {txt}{c |}{res}     1,097 
           {txt}{c |}{res}     48.95      51.05 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}    100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}2{txt}) = {res}129.7833  {txt} Pr = {res}0.000
{txt}
{com}.         tab w19_retro pid_29, row col chi2
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

Econ Worse {c |}
 than Jan. {c |}
      2009 {c |}       PID (W9)
    (W19)? {c |} Republica   Democrat {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
     Worse {c |}{res}       687        418 {txt}{c |}{res}     1,105 
           {txt}{c |}{res}     62.17      37.83 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     73.63      41.39 {txt}{c |}{res}     56.87 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
      Same {c |}{res}       190        402 {txt}{c |}{res}       592 
           {txt}{c |}{res}     32.09      67.91 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     20.36      39.80 {txt}{c |}{res}     30.47 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
    Better {c |}{res}        56        190 {txt}{c |}{res}       246 
           {txt}{c |}{res}     22.76      77.24 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}      6.00      18.81 {txt}{c |}{res}     12.66 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       933      1,010 {txt}{c |}{res}     1,943 
           {txt}{c |}{res}     48.02      51.98 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}    100.00     100.00 {txt}{c |}{res}    100.00 

{txt}          Pearson chi2({res}2{txt}) = {res}211.6768  {txt} Pr = {res}0.000
{txt}
{com}.                 
.                 
.                 
.                 
.                 *****************************************************
.                 *********Network Size and Disagreement***************
.                 *****************************************************
. 
. *# Names given/asked about
.         tab DER17

       {txt}der17. DERIVED. Number of names {c |}
                    mentioned at W9ZD2 {c |}      Freq.     Percent        Cum.
{hline 39}{c +}{hline 35}
      -6. Not asked, unit non-response {c |}{res}      1,591       37.52       37.52
{txt}                      -1. Inapplicable {c |}{res}        446       10.52       48.04
{txt}                0. mentioned   0 names {c |}{res}         65        1.53       49.58
{txt}                 1. mentioned   1 name {c |}{res}        104        2.45       52.03
{txt}                2. mentioned   2 names {c |}{res}        127        3.00       55.02
{txt}                3. mentioned   3 names {c |}{res}        194        4.58       59.60
{txt}                4. mentioned   4 names {c |}{res}        208        4.91       64.50
{txt}                5. mentioned   5 names {c |}{res}        193        4.55       69.06
{txt}                6. mentioned   6 names {c |}{res}        183        4.32       73.37
{txt}                7. mentioned   7 names {c |}{res}        126        2.97       76.34
{txt}                8. mentioned   8 names {c |}{res}      1,003       23.66      100.00
{txt}{hline 39}{c +}{hline 35}
                                 Total {c |}{res}      4,240      100.00
{txt}
{com}.         gen numgiven = DER17
{txt}
{com}.         replace numgiven = . if numgiven < 0
{txt}(2037 real changes made, 2037 to missing)

{com}.         label var numgiven "Number of Listed Discussants"
{txt}
{com}.         tab numgiven 

  {txt}Number of {c |}
     Listed {c |}
Discussants {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}         65        2.95        2.95
{txt}          1 {c |}{res}        104        4.72        7.67
{txt}          2 {c |}{res}        127        5.76       13.44
{txt}          3 {c |}{res}        194        8.81       22.24
{txt}          4 {c |}{res}        208        9.44       31.68
{txt}          5 {c |}{res}        193        8.76       40.44
{txt}          6 {c |}{res}        183        8.31       48.75
{txt}          7 {c |}{res}        126        5.72       54.47
{txt}          8 {c |}{res}      1,003       45.53      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,203      100.00
{txt}
{com}.         summ numgiven, detail

                {txt}Number of Listed Discussants
{hline 61}
      Percentiles      Smallest
 1%    {res}        0              0
{txt} 5%    {res}        1              0
{txt}10%    {res}        2              0       {txt}Obs         {res}       2203
{txt}25%    {res}        4              0       {txt}Sum of Wgt. {res}       2203

{txt}50%    {res}        7                      {txt}Mean          {res} 5.783477
                        {txt}Largest       Std. Dev.     {res} 2.501382
{txt}75%    {res}        8              8
{txt}90%    {res}        8              8       {txt}Variance      {res} 6.256911
{txt}95%    {res}        8              8       {txt}Skewness      {res}-.7296673
{txt}99%    {res}        8              8       {txt}Kurtosis      {res} 2.222504
{txt}
{com}.                 
.         
.         gen numgiven1 = numgiven
{txt}(2037 missing values generated)

{com}.         replace numgiven1 = 3 if numgiven > 3 & numgiven <=8
{txt}(1713 real changes made)

{com}.         label var numgiven1 "Number of Disc. Asked About"
{txt}
{com}.         tab numgiven1

  {txt}Number of {c |}
Disc. Asked {c |}
      About {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}         65        2.95        2.95
{txt}          1 {c |}{res}        104        4.72        7.67
{txt}          2 {c |}{res}        127        5.76       13.44
{txt}          3 {c |}{res}      1,907       86.56      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,203      100.00
{txt}
{com}.         tab numgiven numgiven1

 {txt}Number of {c |}
    Listed {c |}
Discussant {c |}         Number of Disc. Asked About
         s {c |}         0          1          2          3 {c |}     Total
{hline 11}{c +}{hline 44}{c +}{hline 10}
         0 {c |}{res}        65          0          0          0 {txt}{c |}{res}        65 
{txt}         1 {c |}{res}         0        104          0          0 {txt}{c |}{res}       104 
{txt}         2 {c |}{res}         0          0        127          0 {txt}{c |}{res}       127 
{txt}         3 {c |}{res}         0          0          0        194 {txt}{c |}{res}       194 
{txt}         4 {c |}{res}         0          0          0        208 {txt}{c |}{res}       208 
{txt}         5 {c |}{res}         0          0          0        193 {txt}{c |}{res}       193 
{txt}         6 {c |}{res}         0          0          0        183 {txt}{c |}{res}       183 
{txt}         7 {c |}{res}         0          0          0        126 {txt}{c |}{res}       126 
{txt}         8 {c |}{res}         0          0          0      1,003 {txt}{c |}{res}     1,003 
{txt}{hline 11}{c +}{hline 44}{c +}{hline 10}
     Total {c |}{res}        65        104        127      1,907 {txt}{c |}{res}     2,203 

{txt}
{com}.         
.         summ numgiven1 

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 3}numgiven1 {c |}{res}      2203    2.759419    .6740594          0          3
{txt}
{com}.         gen numgiven01 = (numgiven1 - r(min))/(r(max)-r(min))
{txt}(2037 missing values generated)

{com}.         label var numgiven01 "Network Size"
{txt}
{com}.         
. *****Partisan Disagreement
.         
.         /**half of respondents asked whether the discussant was a republican, 
>                 half asked if a Democrat**/
.         
.         foreach var in W9ZD12_1 W9ZD13_1 W9ZD12_2 W9ZD13_2 W9ZD12_3 W9ZD13_3 {c -(}
{txt}  2{com}.                 tab `var'
{txt}  3{com}.                 {c )-}

   {txt}w9zd12_1. Is name1 a Democrat {c |}      Freq.     Percent        Cum.
{hline 33}{c +}{hline 35}
-6. Not asked, unit non-response {c |}{res}      1,474       34.76       34.76
{txt}       -5. Not asked, terminated {c |}{res}        153        3.61       38.37
{txt}                -1. Inapplicable {c |}{res}      1,546       36.46       74.83
{txt}                     1. Democrat {c |}{res}        432       10.19       85.02
{txt}                   2. Republican {c |}{res}        400        9.43       94.46
{txt}                  3. independent {c |}{res}        182        4.29       98.75
{txt}               4. something else {c |}{res}         53        1.25      100.00
{txt}{hline 33}{c +}{hline 35}
                           Total {c |}{res}      4,240      100.00

 {txt}w9zd13_1. Is name1 a Republican {c |}      Freq.     Percent        Cum.
{hline 33}{c +}{hline 35}
-6. Not asked, unit non-response {c |}{res}      1,474       34.76       34.76
{txt}       -5. Not asked, terminated {c |}{res}        180        4.25       39.01
{txt}                -1. Inapplicable {c |}{res}      1,555       36.67       75.68
{txt}                   1. Republican {c |}{res}        382        9.01       84.69
{txt}                     2. Democrat {c |}{res}        432       10.19       94.88
{txt}                  3. independent {c |}{res}        173        4.08       98.96
{txt}               4. something else {c |}{res}         44        1.04      100.00
{txt}{hline 33}{c +}{hline 35}
                           Total {c |}{res}      4,240      100.00

   {txt}w9zd12_2. Is name2 a Democrat {c |}      Freq.     Percent        Cum.
{hline 33}{c +}{hline 35}
-6. Not asked, unit non-response {c |}{res}      1,474       34.76       34.76
{txt}       -5. Not asked, terminated {c |}{res}        153        3.61       38.37
{txt}                -1. Inapplicable {c |}{res}      1,601       37.76       76.13
{txt}                     1. Democrat {c |}{res}        410        9.67       85.80
{txt}                   2. Republican {c |}{res}        361        8.51       94.32
{txt}                  3. independent {c |}{res}        179        4.22       98.54
{txt}               4. something else {c |}{res}         62        1.46      100.00
{txt}{hline 33}{c +}{hline 35}
                           Total {c |}{res}      4,240      100.00

 {txt}w9zd13_2. Is name2 a Republican {c |}      Freq.     Percent        Cum.
{hline 33}{c +}{hline 35}
-6. Not asked, unit non-response {c |}{res}      1,474       34.76       34.76
{txt}       -5. Not asked, terminated {c |}{res}        180        4.25       39.01
{txt}                -1. Inapplicable {c |}{res}      1,607       37.90       76.91
{txt}                   1. Republican {c |}{res}        358        8.44       85.35
{txt}                     2. Democrat {c |}{res}        403        9.50       94.86
{txt}                  3. independent {c |}{res}        163        3.84       98.70
{txt}               4. something else {c |}{res}         55        1.30      100.00
{txt}{hline 33}{c +}{hline 35}
                           Total {c |}{res}      4,240      100.00

   {txt}w9zd12_3. Is name3 a Democrat {c |}      Freq.     Percent        Cum.
{hline 33}{c +}{hline 35}
-6. Not asked, unit non-response {c |}{res}      1,474       34.76       34.76
{txt}       -5. Not asked, terminated {c |}{res}        154        3.63       38.40
{txt}                -1. Inapplicable {c |}{res}      1,665       39.27       77.67
{txt}                     1. Democrat {c |}{res}        407        9.60       87.26
{txt}                   2. Republican {c |}{res}        324        7.64       94.91
{txt}                  3. independent {c |}{res}        156        3.68       98.58
{txt}               4. something else {c |}{res}         60        1.42      100.00
{txt}{hline 33}{c +}{hline 35}
                           Total {c |}{res}      4,240      100.00

 {txt}w9zd13_3. Is name3 a Republican {c |}      Freq.     Percent        Cum.
{hline 33}{c +}{hline 35}
-6. Not asked, unit non-response {c |}{res}      1,474       34.76       34.76
{txt}       -5. Not asked, terminated {c |}{res}        180        4.25       39.01
{txt}                -1. Inapplicable {c |}{res}      1,666       39.29       78.30
{txt}                   1. Republican {c |}{res}        329        7.76       86.06
{txt}                     2. Democrat {c |}{res}        390        9.20       95.26
{txt}                  3. independent {c |}{res}        151        3.56       98.82
{txt}               4. something else {c |}{res}         50        1.18      100.00
{txt}{hline 33}{c +}{hline 35}
                           Total {c |}{res}      4,240      100.00
{txt}
{com}.         
.         *For 12_:
.                 *1 = Dem
.                 *2 = Republican
.                 *3 = ind
.                 *4 = something else
.                 
.         *For 13_: 
.                 *1: Republican
.                 *2 = Dem
.                 *3 = Ind
.                 *4 = something else
.         
.         foreach var in W9ZD12_1 W9ZD12_2 W9ZD12_3 W9ZD13_2 W9ZD12_3 W9ZD13_3  {c -(}
{txt}  2{com}.                 tab `var' if `var' >0
{txt}  3{com}.                 {c )-}

   {txt}w9zd12_1. Is name1 a Democrat {c |}      Freq.     Percent        Cum.
{hline 33}{c +}{hline 35}
                     1. Democrat {c |}{res}        432       40.49       40.49
{txt}                   2. Republican {c |}{res}        400       37.49       77.98
{txt}                  3. independent {c |}{res}        182       17.06       95.03
{txt}               4. something else {c |}{res}         53        4.97      100.00
{txt}{hline 33}{c +}{hline 35}
                           Total {c |}{res}      1,067      100.00

   {txt}w9zd12_2. Is name2 a Democrat {c |}      Freq.     Percent        Cum.
{hline 33}{c +}{hline 35}
                     1. Democrat {c |}{res}        410       40.51       40.51
{txt}                   2. Republican {c |}{res}        361       35.67       76.19
{txt}                  3. independent {c |}{res}        179       17.69       93.87
{txt}               4. something else {c |}{res}         62        6.13      100.00
{txt}{hline 33}{c +}{hline 35}
                           Total {c |}{res}      1,012      100.00

   {txt}w9zd12_3. Is name3 a Democrat {c |}      Freq.     Percent        Cum.
{hline 33}{c +}{hline 35}
                     1. Democrat {c |}{res}        407       42.98       42.98
{txt}                   2. Republican {c |}{res}        324       34.21       77.19
{txt}                  3. independent {c |}{res}        156       16.47       93.66
{txt}               4. something else {c |}{res}         60        6.34      100.00
{txt}{hline 33}{c +}{hline 35}
                           Total {c |}{res}        947      100.00

 {txt}w9zd13_2. Is name2 a Republican {c |}      Freq.     Percent        Cum.
{hline 33}{c +}{hline 35}
                   1. Republican {c |}{res}        358       36.57       36.57
{txt}                     2. Democrat {c |}{res}        403       41.16       77.73
{txt}                  3. independent {c |}{res}        163       16.65       94.38
{txt}               4. something else {c |}{res}         55        5.62      100.00
{txt}{hline 33}{c +}{hline 35}
                           Total {c |}{res}        979      100.00

   {txt}w9zd12_3. Is name3 a Democrat {c |}      Freq.     Percent        Cum.
{hline 33}{c +}{hline 35}
                     1. Democrat {c |}{res}        407       42.98       42.98
{txt}                   2. Republican {c |}{res}        324       34.21       77.19
{txt}                  3. independent {c |}{res}        156       16.47       93.66
{txt}               4. something else {c |}{res}         60        6.34      100.00
{txt}{hline 33}{c +}{hline 35}
                           Total {c |}{res}        947      100.00

 {txt}w9zd13_3. Is name3 a Republican {c |}      Freq.     Percent        Cum.
{hline 33}{c +}{hline 35}
                   1. Republican {c |}{res}        329       35.76       35.76
{txt}                     2. Democrat {c |}{res}        390       42.39       78.15
{txt}                  3. independent {c |}{res}        151       16.41       94.57
{txt}               4. something else {c |}{res}         50        5.43      100.00
{txt}{hline 33}{c +}{hline 35}
                           Total {c |}{res}        920      100.00
{txt}
{com}.                 
.         *Creating PID Scale, inc. leaners*
.                 *1 = Democrat, 2 = Republican; 3 = Ind
.                 
.                 label def dpid 1 "Democrat" 2 "Republican" 3 "Independent"
{txt}
{com}.                 
.                 
.                 gen d1_pid = . 
{txt}(4240 missing values generated)

{com}.                 replace d1_pid = 1 if W9ZD12_1 == 1 
{txt}(432 real changes made)

{com}.                 replace d1_pid = 1 if W9ZD12_1 == 3 & W9ZD16_1 == 1
{txt}(57 real changes made)

{com}.                 replace d1_pid = 1 if W9ZD12_1 == 4 & W9ZD16_1 == 1
{txt}(10 real changes made)

{com}.                 replace d1_pid = 2 if W9ZD12_1 == 2
{txt}(400 real changes made)

{com}.                 replace d1_pid = 2 if W9ZD12_1 == 3 & W9ZD16_1 == 2
{txt}(43 real changes made)

{com}.                 replace d1_pid = 2 if W9ZD12_1 == 4 & W9ZD16_1 == 2
{txt}(7 real changes made)

{com}.                 replace d1_pid = 3 if W9ZD12_1 == 3 & W9ZD16_1 == 3
{txt}(73 real changes made)

{com}.                 replace d1_pid = 3 if W9ZD12_1 == 4 & W9ZD16_1 == 3
{txt}(34 real changes made)

{com}.                 replace d1_pid = 2 if W9ZD13_1 == 1 
{txt}(382 real changes made)

{com}.                 replace d1_pid = 2 if W9ZD13_1 == 3 & W9ZD16_1 == 1
{txt}(50 real changes made)

{com}.                 replace d1_pid = 2 if W9ZD13_1 == 4 & W9ZD16_1 == 1
{txt}(9 real changes made)

{com}.                 replace d1_pid = 1 if W9ZD13_1 == 2
{txt}(432 real changes made)

{com}.                 replace d1_pid = 1 if W9ZD13_1 == 3 & W9ZD16_1 == 2
{txt}(44 real changes made)

{com}.                 replace d1_pid = 1 if W9ZD13_1 == 4 & W9ZD16_1 == 2
{txt}(8 real changes made)

{com}.                 replace d1_pid = 3 if W9ZD13_1 == 3 & W9ZD16_1 == 3
{txt}(79 real changes made)

{com}.                 replace d1_pid = 3 if W9ZD13_1 == 4 & W9ZD16_1 == 3
{txt}(27 real changes made)

{com}.                 
.                 
.                 gen d2_pid = . 
{txt}(4240 missing values generated)

{com}.                 replace d2_pid = 1 if W9ZD12_2 == 1 
{txt}(410 real changes made)

{com}.                 replace d2_pid = 1 if W9ZD12_2 == 3 & W9ZD16_2 == 1
{txt}(55 real changes made)

{com}.                 replace d2_pid = 1 if W9ZD12_2 == 4 & W9ZD16_2 == 1
{txt}(12 real changes made)

{com}.                 
.                 replace d2_pid = 2 if W9ZD12_2 == 2
{txt}(361 real changes made)

{com}.                 replace d2_pid = 2 if W9ZD12_2 == 3 & W9ZD16_2 == 2
{txt}(44 real changes made)

{com}.                 replace d2_pid = 2 if W9ZD12_2 == 4 & W9ZD16_2 == 2
{txt}(7 real changes made)

{com}. 
.                 replace d2_pid = 3 if W9ZD12_2 == 3 & W9ZD16_2 == 3
{txt}(75 real changes made)

{com}.                 replace d2_pid = 3 if W9ZD12_2 == 4 & W9ZD16_2 == 3
{txt}(40 real changes made)

{com}. 
.                 replace d2_pid = 2 if W9ZD13_2 == 1 
{txt}(358 real changes made)

{com}.                 replace d2_pid = 2 if W9ZD13_2 == 3 & W9ZD16_2 == 1
{txt}(46 real changes made)

{com}.                 replace d2_pid = 2 if W9ZD13_2 == 4 & W9ZD16_2 == 1
{txt}(8 real changes made)

{com}.                 
.                 replace d2_pid = 1 if W9ZD13_2 == 2
{txt}(403 real changes made)

{com}.                 replace d2_pid = 1 if W9ZD13_2 == 3 & W9ZD16_2 == 2
{txt}(47 real changes made)

{com}.                 replace d2_pid = 1 if W9ZD13_2 == 4 & W9ZD16_2 == 2
{txt}(6 real changes made)

{com}. 
.                 replace d2_pid = 3 if W9ZD13_2 == 3 & W9ZD16_2 == 3
{txt}(70 real changes made)

{com}.                 replace d2_pid = 3 if W9ZD13_2 == 4 & W9ZD16_2 == 3
{txt}(41 real changes made)

{com}.                 
.                 
.                 gen d3_pid = . 
{txt}(4240 missing values generated)

{com}.                 replace d3_pid = 1 if W9ZD12_3 == 1 
{txt}(407 real changes made)

{com}.                 replace d3_pid = 1 if W9ZD12_3 == 3 & W9ZD16_3 == 1
{txt}(46 real changes made)

{com}.                 replace d3_pid = 1 if W9ZD12_3 == 4 & W9ZD16_3 == 1
{txt}(13 real changes made)

{com}.                 
.                 replace d3_pid = 2 if W9ZD12_3 == 2
{txt}(324 real changes made)

{com}.                 replace d3_pid = 2 if W9ZD12_3 == 3 & W9ZD16_3 == 2
{txt}(37 real changes made)

{com}.                 replace d3_pid = 2 if W9ZD12_3 == 4 & W9ZD16_3 == 2
{txt}(5 real changes made)

{com}. 
.                 replace d3_pid = 3 if W9ZD12_3 == 3 & W9ZD16_3 == 3
{txt}(67 real changes made)

{com}.                 replace d3_pid = 3 if W9ZD12_3 == 4 & W9ZD16_3 == 3
{txt}(42 real changes made)

{com}. 
.                 replace d3_pid = 2 if W9ZD13_3 == 1 
{txt}(329 real changes made)

{com}.                 replace d3_pid = 2 if W9ZD13_3 == 3 & W9ZD16_3 == 1
{txt}(47 real changes made)

{com}.                 replace d3_pid = 2 if W9ZD13_3 == 4 & W9ZD16_3 == 1
{txt}(4 real changes made)

{com}.                 
.                 replace d3_pid = 1 if W9ZD13_3 == 2
{txt}(390 real changes made)

{com}.                 replace d3_pid = 1 if W9ZD13_3 == 3 & W9ZD16_3 == 2
{txt}(45 real changes made)

{com}.                 replace d3_pid = 1 if W9ZD13_3 == 4 & W9ZD16_3 == 2
{txt}(11 real changes made)

{com}. 
.                 replace d3_pid = 3 if W9ZD13_3 == 3 & W9ZD16_3 == 3
{txt}(58 real changes made)

{com}.                 replace d3_pid = 3 if W9ZD13_3 == 4 & W9ZD16_3 == 3
{txt}(34 real changes made)

{com}.                 
.                 label values d1_pid d2_pid d3_pid  dpid
{txt}
{com}.                 
.         *Agreement
.                 *1 = Dem/Dem, Ind/Ind, Rep/Rep
.                 *0 = Dem/Rep, Dem/Something Else, Dem/Ind
.                 *0= Rep/Dem,. Rep/Something Else, rep/Ind
.                 *0 = Ind/Dem, Ind/Rep
.                 label def ag 1 "Agree" 0 "Disagree" 
{txt}
{com}.                 
.                 gen d1_agree = . 
{txt}(4240 missing values generated)

{com}.                 replace d1_agree = 1 if pid_39 == 1 & d1_pid == 1
{txt}(706 real changes made)

{com}.                 replace d1_agree = 1 if pid_39 == 2 & d1_pid == 2
{txt}(616 real changes made)

{com}.                 replace d1_agree = 1 if pid_39 == 3 & d1_pid == 3
{txt}(75 real changes made)

{com}.                 replace d1_agree = 0 if pid_39 == 1 & d1_pid == 2
{txt}(238 real changes made)

{com}.                 replace d1_agree = 0 if pid_39 == 1 & d1_pid == 3
{txt}(63 real changes made)

{com}.                 replace d1_agree = 0 if pid_39 == 2 & d1_pid == 1
{txt}(214 real changes made)

{com}.                 replace d1_agree = 0 if pid_39 == 2 & d1_pid == 3
{txt}(75 real changes made)

{com}.                 replace d1_agree = 0 if pid_39 == 3 & d1_pid == 1
{txt}(63 real changes made)

{com}.                 replace d1_agree = 0 if pid_39 == 3 & d1_pid == 2
{txt}(37 real changes made)

{com}.                 label var d1_agree "D1 Agree?"
{txt}
{com}.                 label values d1_agree ag
{txt}
{com}.                 
. 
.                 gen d2_agree = .
{txt}(4240 missing values generated)

{com}.                 replace d2_agree = 1 if pid_39 == 1 & d2_pid == 1
{txt}(654 real changes made)

{com}.                 replace d2_agree = 1 if pid_39 == 2 & d2_pid == 2
{txt}(560 real changes made)

{com}.                 replace d2_agree = 1 if pid_39 == 3 & d2_pid == 3
{txt}(65 real changes made)

{com}.                 
.                 replace d2_agree = 0 if pid_39 == 1 & d2_pid == 2
{txt}(223 real changes made)

{com}.                 replace d2_agree = 0 if pid_39 == 1 & d2_pid == 3
{txt}(83 real changes made)

{com}.                 replace d2_agree = 0 if pid_39 == 2 & d2_pid == 1
{txt}(224 real changes made)

{com}.                 replace d2_agree = 0 if pid_39 == 2 & d2_pid == 3
{txt}(78 real changes made)

{com}.                 replace d2_agree = 0 if pid_39 == 3 & d2_pid == 1
{txt}(55 real changes made)

{com}.                 replace d2_agree = 0 if pid_39 == 3 & d2_pid == 2
{txt}(41 real changes made)

{com}.                         
.                 label var d2_agree "D2 Agree?"
{txt}
{com}.                 label values d2_agree ag
{txt}
{com}.                 
.                 
.                 gen d3_agree = .
{txt}(4240 missing values generated)

{com}.                 replace d3_agree = 1 if pid_39 == 1 & d3_pid == 1
{txt}(625 real changes made)

{com}.                 replace d3_agree = 1 if pid_39 == 2 & d3_pid == 2
{txt}(506 real changes made)

{com}.                 replace d3_agree = 1 if pid_39 == 3 & d3_pid == 3
{txt}(51 real changes made)

{com}.                 
.                 replace d3_agree = 0 if pid_39 == 1 & d3_pid == 2
{txt}(204 real changes made)

{com}.                 replace d3_agree = 0 if pid_39 == 1 & d3_pid == 3
{txt}(70 real changes made)

{com}.                 replace d3_agree = 0 if pid_39 == 2 & d3_pid == 1
{txt}(231 real changes made)

{com}.                 replace d3_agree = 0 if pid_39 == 2 & d3_pid == 3
{txt}(80 real changes made)

{com}.                 replace d3_agree = 0 if pid_39 == 3 & d3_pid == 1
{txt}(56 real changes made)

{com}.                 replace d3_agree = 0 if pid_39 == 3 & d3_pid == 2
{txt}(36 real changes made)

{com}.                 label var d3_agree "D3 Agree?"
{txt}
{com}.                 label values d3_agree ag
{txt}
{com}.                 
.                 tab d1_agree

  {txt}D1 Agree? {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
   Disagree {c |}{res}        690       33.06       33.06
{txt}      Agree {c |}{res}      1,397       66.94      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,087      100.00
{txt}
{com}.                 tab d2_agree

  {txt}D2 Agree? {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
   Disagree {c |}{res}        704       35.50       35.50
{txt}      Agree {c |}{res}      1,279       64.50      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,983      100.00
{txt}
{com}.                 tab d3_agree

  {txt}D3 Agree? {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
   Disagree {c |}{res}        677       36.42       36.42
{txt}      Agree {c |}{res}      1,182       63.58      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,859      100.00
{txt}
{com}.                 tab d1_agree d2_agree, row col
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

           {c |}       D2 Agree?
 D1 Agree? {c |}  Disagree      Agree {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
  Disagree {c |}{res}       358        289 {txt}{c |}{res}       647 
           {txt}{c |}{res}     55.33      44.67 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     51.07      22.67 {txt}{c |}{res}     32.74 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Agree {c |}{res}       343        986 {txt}{c |}{res}     1,329 
           {txt}{c |}{res}     25.81      74.19 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     48.93      77.33 {txt}{c |}{res}     67.26 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       701      1,275 {txt}{c |}{res}     1,976 
           {txt}{c |}{res}     35.48      64.52 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}    100.00     100.00 {txt}{c |}{res}    100.00 

{txt}
{com}.                 tab d1_agree d3_agree, row col
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

           {c |}       D3 Agree?
 D1 Agree? {c |}  Disagree      Agree {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
  Disagree {c |}{res}       323        283 {txt}{c |}{res}       606 
           {txt}{c |}{res}     53.30      46.70 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     48.07      24.00 {txt}{c |}{res}     32.74 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Agree {c |}{res}       349        896 {txt}{c |}{res}     1,245 
           {txt}{c |}{res}     28.03      71.97 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     51.93      76.00 {txt}{c |}{res}     67.26 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       672      1,179 {txt}{c |}{res}     1,851 
           {txt}{c |}{res}     36.30      63.70 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}    100.00     100.00 {txt}{c |}{res}    100.00 

{txt}
{com}.                 tab d2_agree d3_agree, row col
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

           {c |}       D3 Agree?
 D2 Agree? {c |}  Disagree      Agree {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
  Disagree {c |}{res}       377        281 {txt}{c |}{res}       658 
           {txt}{c |}{res}     57.29      42.71 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     55.93      23.85 {txt}{c |}{res}     35.53 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Agree {c |}{res}       297        897 {txt}{c |}{res}     1,194 
           {txt}{c |}{res}     24.87      75.13 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}     44.07      76.15 {txt}{c |}{res}     64.47 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}       674      1,178 {txt}{c |}{res}     1,852 
           {txt}{c |}{res}     36.39      63.61 {txt}{c |}{res}    100.00 
           {txt}{c |}{res}    100.00     100.00 {txt}{c |}{res}    100.00 

{txt}
{com}.         
.         *Disagreement
.                 *0 = Dem/Dem, Ind/Ind, Rep/Rep
.                 *1 = Dem/Rep, Dem/Something Else, Dem/Ind
.                 *1= Rep/Dem,. Rep/Something Else, rep/Ind
.                 *1 = Ind/Dem, Ind/Rep
.                 
.                 label def disag1 1 "Disagree" 0 "Aggree" 
{txt}
{com}. 
.                 omscore d1_agree
{txt}Reverse recoding ok. New variabele = rr_d1_agree

{com}.                 rename rr_d1_agree d1_disagree
{res}{txt}
{com}.                 label var d1_disagree "D1 Agree?"
{txt}
{com}.                 label values d1_disagree disag1
{txt}
{com}.                 
.                 omscore d2_agree
{txt}Reverse recoding ok. New variabele = rr_d2_agree

{com}.                 rename rr_d2_agree d2_disagree          
{res}{txt}
{com}.                 label var d2_disagree "D2 Agree?"
{txt}
{com}.                 label values d2_disagree disag1
{txt}
{com}.                 
.                 omscore d3_agree
{txt}Reverse recoding ok. New variabele = rr_d3_agree

{com}.                 rename rr_d3_agree d3_disagree
{res}{txt}
{com}.                 label var d3_disagree "D3 Agree?"
{txt}
{com}.                 label values d3_disagree disag1
{txt}
{com}.                 
.                 
.                 tab d1_agree d1_disagree

           {txt}{c |}       D1 Agree?
 D1 Agree? {c |}    Aggree   Disagree {c |}     Total
{hline 11}{c +}{hline 22}{c +}{hline 10}
  Disagree {c |}{res}         0        690 {txt}{c |}{res}       690 
{txt}     Agree {c |}{res}     1,397          0 {txt}{c |}{res}     1,397 
{txt}{hline 11}{c +}{hline 22}{c +}{hline 10}
     Total {c |}{res}     1,397        690 {txt}{c |}{res}     2,087 

{txt}
{com}. 
.                 
.         *Summary and Average
.         *Summary Agreement
.                 egen pid_agree = rowtotal(d1_agree d2_agree d3_agree), missing
{txt}(2144 missing values generated)

{com}.         *Disagreement
.                 egen pid_disagree = rowtotal(d1_disagree d2_disagree d3_disagree), missing
{txt}(2144 missing values generated)

{com}.         
.         *Summary Scale
.                         *See Lupton and Thornton: Exposure = D - A
.                         gen disagree_total = pid_disagree - pid_agree
{txt}(2144 missing values generated)

{com}.                         label var disagree_total "Network Disagreement"
{txt}
{com}.                 *Divided by network size
.                         *See Lupton and Thonrton: (D-A)/(D+A)
.                         gen disagree_avg = [pid_disagree - pid_agree]/[pid_disagree + pid_agree]
{txt}(2144 missing values generated)

{com}.                         label var disagree_avg "Network Disagreement"
{txt}
{com}.                 
.                 
.         *Diversity Measure*
.                 *From Nir (2005): [(Agree+Disagree)/2] - |A-D|
.                         gen network_ambiv = [(pid_agree+pid_disagree)/2] - abs(pid_agree - pid_disagree)
{txt}(2144 missing values generated)

{com}.                         label var network_ambiv "Network Political Diversity"
{txt}
{com}.                 
.                         summ network_ambiv

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
network_am~v {c |}{res}      2096   -.5126431    .9714087       -1.5          1
{txt}
{com}.                         gen network_ambiv01=(network_ambiv - r(min))/(r(max)-r(min))
{txt}(2144 missing values generated)

{com}.                         label var network_ambiv01 "Network Political Diversity" 
{txt}
{com}.                 
. 
.         
. *General Agreement
.         label def gendifference 1 "Not Different at All" 2 "Slightly Different" 3 "Moderately Different" 4 "Very Different" 5 "Extremely Different"
{txt}
{com}.         gen gendiff1 = . 
{txt}(4240 missing values generated)

{com}.         replace gendiff1 = 1 if W9ZD9_1 == 5
{txt}(588 real changes made)

{com}.         replace gendiff1 = 2 if W9ZD9_1 == 4
{txt}(829 real changes made)

{com}.         replace gendiff1 = 3 if W9ZD9_1 == 3
{txt}(431 real changes made)

{com}.         replace gendiff1 = 4 if W9ZD9_1 == 2
{txt}(186 real changes made)

{com}.         replace gendiff1 = 5 if W9ZD9_1 == 1
{txt}(82 real changes made)

{com}.         label var gendiff1 "General Difference with Disc. 1"
{txt}
{com}.         label values gendiff1 gendifference
{txt}
{com}.         tab gendiff1 W9ZD9_1

  {txt}General Difference {c |}  w9zd9_1. Difference in opinions between R and name1
        with Disc. 1 {c |} 1. Extrem  2. Very d  3. Modera  4. Slight  5. Not di {c |}     Total
{hline 21}{c +}{hline 55}{c +}{hline 10}
Not Different at All {c |}{res}         0          0          0          0        588 {txt}{c |}{res}       588 
{txt}  Slightly Different {c |}{res}         0          0          0        829          0 {txt}{c |}{res}       829 
{txt}Moderately Different {c |}{res}         0          0        431          0          0 {txt}{c |}{res}       431 
{txt}      Very Different {c |}{res}         0        186          0          0          0 {txt}{c |}{res}       186 
{txt} Extremely Different {c |}{res}        82          0          0          0          0 {txt}{c |}{res}        82 
{txt}{hline 21}{c +}{hline 55}{c +}{hline 10}
               Total {c |}{res}        82        186        431        829        588 {txt}{c |}{res}     2,116 

{txt}
{com}.         
.         
.         gen gendiff2 = . 
{txt}(4240 missing values generated)

{com}.         replace gendiff2 = 1 if W9ZD9_2 == 5
{txt}(560 real changes made)

{com}.         replace gendiff2 = 2 if W9ZD9_2 == 4
{txt}(742 real changes made)

{com}.         replace gendiff2 = 3 if W9ZD9_2 == 3
{txt}(456 real changes made)

{com}.         replace gendiff2 = 4 if W9ZD9_2 == 2
{txt}(165 real changes made)

{com}.         replace gendiff2 = 5 if W9ZD9_2 == 1
{txt}(89 real changes made)

{com}.         label var gendiff2 "General Difference with Disc. 2"
{txt}
{com}.         label values gendiff2 gendifference
{txt}
{com}.                 tab gendiff2 W9ZD9_2

  {txt}General Difference {c |}  w9zd9_2. Difference in opinions between R and name2
        with Disc. 2 {c |} 1. Extrem  2. Very d  3. Modera  4. Slight  5. Not di {c |}     Total
{hline 21}{c +}{hline 55}{c +}{hline 10}
Not Different at All {c |}{res}         0          0          0          0        560 {txt}{c |}{res}       560 
{txt}  Slightly Different {c |}{res}         0          0          0        742          0 {txt}{c |}{res}       742 
{txt}Moderately Different {c |}{res}         0          0        456          0          0 {txt}{c |}{res}       456 
{txt}      Very Different {c |}{res}         0        165          0          0          0 {txt}{c |}{res}       165 
{txt} Extremely Different {c |}{res}        89          0          0          0          0 {txt}{c |}{res}        89 
{txt}{hline 21}{c +}{hline 55}{c +}{hline 10}
               Total {c |}{res}        89        165        456        742        560 {txt}{c |}{res}     2,012 

{txt}
{com}. 
.         
.         gen gendiff3 = . 
{txt}(4240 missing values generated)

{com}.         replace gendiff3 = 1 if W9ZD9_3 == 5
{txt}(491 real changes made)

{com}.         replace gendiff3 = 2 if W9ZD9_3 == 4
{txt}(698 real changes made)

{com}.         replace gendiff3 = 3 if W9ZD9_3 == 3
{txt}(449 real changes made)

{com}.         replace gendiff3 = 4 if W9ZD9_3 == 2
{txt}(156 real changes made)

{com}.         replace gendiff3 = 5 if W9ZD9_3 == 1
{txt}(88 real changes made)

{com}.         label var gendiff3 "General Difference with Disc. 3"
{txt}
{com}.         label values gendiff3 gendifference
{txt}
{com}.         tab gendiff3 W9ZD9_3

  {txt}General Difference {c |}  w9zd9_3. Difference in opinions between R and name3
        with Disc. 3 {c |} 1. Extrem  2. Very d  3. Modera  4. Slight  5. Not di {c |}     Total
{hline 21}{c +}{hline 55}{c +}{hline 10}
Not Different at All {c |}{res}         0          0          0          0        491 {txt}{c |}{res}       491 
{txt}  Slightly Different {c |}{res}         0          0          0        698          0 {txt}{c |}{res}       698 
{txt}Moderately Different {c |}{res}         0          0        449          0          0 {txt}{c |}{res}       449 
{txt}      Very Different {c |}{res}         0        156          0          0          0 {txt}{c |}{res}       156 
{txt} Extremely Different {c |}{res}        88          0          0          0          0 {txt}{c |}{res}        88 
{txt}{hline 21}{c +}{hline 55}{c +}{hline 10}
               Total {c |}{res}        88        156        449        698        491 {txt}{c |}{res}     1,882 

{txt}
{com}.         egen gendiff = rowmean(gendiff1 gendiff2 gendiff3)
{txt}(2123 missing values generated)

{com}.         label var gendiff "General Disagreement"
{txt}
{com}.         tab gendiff1

  {txt}General Difference {c |}
        with Disc. 1 {c |}      Freq.     Percent        Cum.
{hline 21}{c +}{hline 35}
Not Different at All {c |}{res}        588       27.79       27.79
{txt}  Slightly Different {c |}{res}        829       39.18       66.97
{txt}Moderately Different {c |}{res}        431       20.37       87.33
{txt}      Very Different {c |}{res}        186        8.79       96.12
{txt} Extremely Different {c |}{res}         82        3.88      100.00
{txt}{hline 21}{c +}{hline 35}
               Total {c |}{res}      2,116      100.00
{txt}
{com}.         tab gendiff2

  {txt}General Difference {c |}
        with Disc. 2 {c |}      Freq.     Percent        Cum.
{hline 21}{c +}{hline 35}
Not Different at All {c |}{res}        560       27.83       27.83
{txt}  Slightly Different {c |}{res}        742       36.88       64.71
{txt}Moderately Different {c |}{res}        456       22.66       87.38
{txt}      Very Different {c |}{res}        165        8.20       95.58
{txt} Extremely Different {c |}{res}         89        4.42      100.00
{txt}{hline 21}{c +}{hline 35}
               Total {c |}{res}      2,012      100.00
{txt}
{com}.         tab gendiff3

  {txt}General Difference {c |}
        with Disc. 3 {c |}      Freq.     Percent        Cum.
{hline 21}{c +}{hline 35}
Not Different at All {c |}{res}        491       26.09       26.09
{txt}  Slightly Different {c |}{res}        698       37.09       63.18
{txt}Moderately Different {c |}{res}        449       23.86       87.04
{txt}      Very Different {c |}{res}        156        8.29       95.32
{txt} Extremely Different {c |}{res}         88        4.68      100.00
{txt}{hline 21}{c +}{hline 35}
               Total {c |}{res}      1,882      100.00
{txt}
{com}.         
. 
. *Relationship, General and Partisan*
.         pwcorr disagree_total disagree_avg gendiff, sig

             {txt}{c |} disagr~l disagr~g  gendiff
{hline 13}{c +}{hline 27}
disagree_t~l {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
disagree_avg {c |} {res}  0.9729   1.0000 
             {txt}{c |}{res}   0.0000
             {txt}{c |}
     gendiff {c |} {res}  0.4291   0.4326   1.0000 
             {txt}{c |}{res}   0.0000   0.0000
             {txt}{c |}

{com}.         ttest gendiff1, by(d1_agree)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
Disagree {c |}{res}{col 12}    690{col 22} 2.876812{col 34} .0428406{col 46} 1.125331{col 58} 2.792698{col 70} 2.960925
   {txt}Agree {c |}{res}{col 12}   1394{col 22}   1.8967{col 34} .0233322{col 46} .8711392{col 58}  1.85093{col 70}  1.94247
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}   2084{col 22} 2.221209{col 34} .0233807{col 46} 1.067349{col 58} 2.175357{col 70} 2.267061
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .9801115{col 34} .0448118{col 58} .8922309{col 70} 1.067992
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}Disagree{txt}) - mean({res}Agree{txt})                           t = {res} 21.8717
{txt}Ho: diff = 0                                     degrees of freedom = {res}    2082

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000
{txt}
{com}.         ttest gendiff2, by(d2_agree)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
Disagree {c |}{res}{col 12}    704{col 22} 2.882102{col 34} .0430967{col 46} 1.143486{col 58} 2.797489{col 70} 2.966716
   {txt}Agree {c |}{res}{col 12}   1278{col 22} 1.898279{col 34} .0242886{col 46} .8682949{col 58} 1.850629{col 70} 1.945928
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}   1982{col 22}  2.24773{col 34} .0243152{col 46} 1.082504{col 58} 2.200044{col 70} 2.295416
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} .9838237{col 34}  .045759{col 58} .8940829{col 70} 1.073564
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}Disagree{txt}) - mean({res}Agree{txt})                           t = {res} 21.5001
{txt}Ho: diff = 0                                     degrees of freedom = {res}    1980

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000
{txt}
{com}.         ttest gendiff3, by(d3_agree)

{txt}Two-sample t test with equal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. Err.{col 47}Std. Dev.{col 59}[95% Conf. Interval]
{hline 9}{c +}{hline 68}
Disagree {c |}{res}{col 12}    677{col 22} 2.929099{col 34} .0425504{col 46} 1.107129{col 58} 2.845552{col 70} 3.012646
   {txt}Agree {c |}{res}{col 12}   1182{col 22} 1.914552{col 34} .0255092{col 46} .8770137{col 58} 1.864503{col 70}   1.9646
{txt}{hline 9}{c +}{hline 68}
combined {c |}{res}{col 12}   1859{col 22} 2.284024{col 34} .0251229{col 46} 1.083201{col 58} 2.234752{col 70} 2.333296
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22} 1.014547{col 34} .0466151{col 58} .9231239{col 70} 1.105971
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}Disagree{txt}) - mean({res}Agree{txt})                           t = {res} 21.7644
{txt}Ho: diff = 0                                     degrees of freedom = {res}    1857

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000
{txt}
{com}. 
. *Combined (old)
.                 gen d1_both = d1_disagree + gendiff1
{txt}(2156 missing values generated)

{com}.                 gen d2_both = d2_disagree + gendiff2
{txt}(2258 missing values generated)

{com}.                 gen d3_both = d3_disagree + gendiff3
{txt}(2381 missing values generated)

{com}.                 tab d1_both

    {txt}d1_both {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}        508       24.38       24.38
{txt}          2 {c |}{res}        675       32.39       56.77
{txt}          3 {c |}{res}        426       20.44       77.21
{txt}          4 {c |}{res}        255       12.24       89.44
{txt}          5 {c |}{res}        157        7.53       96.98
{txt}          6 {c |}{res}         63        3.02      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,084      100.00
{txt}
{com}.                 tab d2_both

    {txt}d2_both {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}        467       23.56       23.56
{txt}          2 {c |}{res}        629       31.74       55.30
{txt}          3 {c |}{res}        389       19.63       74.92
{txt}          4 {c |}{res}        293       14.78       89.71
{txt}          5 {c |}{res}        129        6.51       96.22
{txt}          6 {c |}{res}         75        3.78      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,982      100.00
{txt}
{com}.                 tab d3_both

    {txt}d3_both {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}        422       22.70       22.70
{txt}          2 {c |}{res}        571       30.72       53.42
{txt}          3 {c |}{res}        384       20.66       74.07
{txt}          4 {c |}{res}        271       14.58       88.65
{txt}          5 {c |}{res}        143        7.69       96.34
{txt}          6 {c |}{res}         68        3.66      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,859      100.00
{txt}
{com}.                 
.                 egen dis_both = rowmean(d1_both d2_both d3_both)
{txt}(2146 missing values generated)

{com}.                 label var dis_both "Index of Network Disagreement"
{txt}
{com}.                 summ dis_both, detail

                {txt}Index of Network Disagreement
{hline 61}
      Percentiles      Smallest
 1%    {res}        1              1
{txt} 5%    {res}        1              1
{txt}10%    {res} 1.333333              1       {txt}Obs         {res}       2094
{txt}25%    {res}        2              1       {txt}Sum of Wgt. {res}       2094

{txt}50%    {res} 2.666667                      {txt}Mean          {res} 2.598615
                        {txt}Largest       Std. Dev.     {res} 1.000358
{txt}75%    {res} 3.333333              6
{txt}90%    {res}        4              6       {txt}Variance      {res} 1.000715
{txt}95%    {res} 4.333333              6       {txt}Skewness      {res} .3907251
{txt}99%    {res}        5              6       {txt}Kurtosis      {res} 2.822583
{txt}
{com}.                 tab dis_both

   {txt}Index of {c |}
    Network {c |}
Disagreemen {c |}
          t {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          1 {c |}{res}        185        8.83        8.83
{txt}   1.333333 {c |}{res}        117        5.59       14.42
{txt}        1.5 {c |}{res}         11        0.53       14.95
{txt}   1.666667 {c |}{res}        164        7.83       22.78
{txt}          2 {c |}{res}        303       14.47       37.25
{txt}   2.333333 {c |}{res}        228       10.89       48.14
{txt}        2.5 {c |}{res}         23        1.10       49.24
{txt}   2.666667 {c |}{res}        215       10.27       59.50
{txt}          3 {c |}{res}        279       13.32       72.83
{txt}   3.333333 {c |}{res}        162        7.74       80.56
{txt}        3.5 {c |}{res}         15        0.72       81.28
{txt}   3.666667 {c |}{res}        157        7.50       88.78
{txt}          4 {c |}{res}        103        4.92       93.70
{txt}   4.333333 {c |}{res}         55        2.63       96.32
{txt}        4.5 {c |}{res}          4        0.19       96.51
{txt}   4.666667 {c |}{res}         23        1.10       97.61
{txt}          5 {c |}{res}         30        1.43       99.04
{txt}   5.333333 {c |}{res}         10        0.48       99.52
{txt}        5.5 {c |}{res}          2        0.10       99.62
{txt}   5.666667 {c |}{res}          4        0.19       99.81
{txt}          6 {c |}{res}          4        0.19      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,094      100.00
{txt}
{com}. 
.                 
.                 *Combined Index (new)
.         /**fn. 5 (Lupton and Thornton: 
>                         A = sum(ai * si) where a = 1 if agree and s = agreement weight
>                         D = sum(di * si) where d = 1 if disagree and s = disagreement weight
>                         Disagreement = D - A**/
.                         
.                 *Agreement Coding*
.                         foreach var in gendiff1 gendiff2 gendiff3 {c -(}
{txt}  2{com}.                                 recode `var' (1=5) (2=4) (3=3) (4=2) (5=1), gen(`var'_ag)
{txt}  3{com}.                                 {c )-}
{txt}(1685 differences between gendiff1 and gendiff1_ag)
(1556 differences between gendiff2 and gendiff2_ag)
(1433 differences between gendiff3 and gendiff3_ag)

{com}.                 *Agree Scale
.                         gen a1 = d1_agree * gendiff1_ag
{txt}(2156 missing values generated)

{com}.                         gen a2 = d2_agree * gendiff2_ag
{txt}(2258 missing values generated)

{com}.                         gen a3 = d3_agree * gendiff3_ag
{txt}(2381 missing values generated)

{com}.                         egen agree_weight = rowtotal(a1 a2 a3), missing
{txt}(2146 missing values generated)

{com}. 
.                 
.                 *Disagree Scale
.                         gen d1 = d1_disagree * gendiff1
{txt}(2156 missing values generated)

{com}.                         gen d2 = d2_disagree * gendiff2
{txt}(2258 missing values generated)

{com}.                         gen d3 = d3_disagree * gendiff3
{txt}(2381 missing values generated)

{com}.                         egen disagree_weight = rowtotal(d1 d2 d3), missing
{txt}(2146 missing values generated)

{com}. 
.                 *Exposure to Disagreement
.                         gen disagree_total_weight = disagree_weight - agree_weight
{txt}(2146 missing values generated)

{com}.                         gen disagree_avg_weight = disagree_total_weight/(pid_disagree+pid_agree)
{txt}(2146 missing values generated)

{com}.                 
.                         foreach var in disagree_total_weight disagree_avg_weight {c -(}
{txt}  2{com}.                                 summ `var' 
{txt}  3{com}.                                 gen `var'01 = (`var' - r(min))/(r(max)-r(min))
{txt}  4{com}.                                 tab `var'01
{txt}  5{com}.                         {c )-}

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
disagree_t~t {c |}{res}      2094   -4.677173    7.310196        -15         15
{txt}(2146 missing values generated)

disagree_to {c |}
tal_weight0 {c |}
          1 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        137        6.54        6.54
{txt}   .0333333 {c |}{res}        105        5.01       11.56
{txt}   .0666667 {c |}{res}        125        5.97       17.53
{txt}         .1 {c |}{res}        169        8.07       25.60
{txt}   .1333333 {c |}{res}         86        4.11       29.70
{txt}   .1666667 {c |}{res}         75        3.58       33.29
{txt}         .2 {c |}{res}         50        2.39       35.67
{txt}   .2333333 {c |}{res}         59        2.82       38.49
{txt}   .2666667 {c |}{res}         72        3.44       41.93
{txt}         .3 {c |}{res}        127        6.06       47.99
{txt}   .3333333 {c |}{res}        141        6.73       54.73
{txt}   .3666667 {c |}{res}        129        6.16       60.89
{txt}         .4 {c |}{res}         87        4.15       65.04
{txt}   .4333333 {c |}{res}         52        2.48       67.53
{txt}   .4666667 {c |}{res}         36        1.72       69.25
{txt}         .5 {c |}{res}         54        2.58       71.82
{txt}   .5333334 {c |}{res}         82        3.92       75.74
{txt}   .5666667 {c |}{res}         80        3.82       79.56
{txt}         .6 {c |}{res}         97        4.63       84.19
{txt}   .6333333 {c |}{res}         71        3.39       87.58
{txt}   .6666667 {c |}{res}         41        1.96       89.54
{txt}         .7 {c |}{res}         42        2.01       91.55
{txt}   .7333333 {c |}{res}         34        1.62       93.17
{txt}   .7666667 {c |}{res}         53        2.53       95.70
{txt}         .8 {c |}{res}         28        1.34       97.04
{txt}   .8333333 {c |}{res}         24        1.15       98.19
{txt}   .8666667 {c |}{res}         14        0.67       98.85
{txt}         .9 {c |}{res}         11        0.53       99.38
{txt}   .9333333 {c |}{res}          8        0.38       99.76
{txt}   .9666666 {c |}{res}          3        0.14       99.90
{txt}          1 {c |}{res}          2        0.10      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,094      100.00

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
disagree_a~t {c |}{res}      2094   -1.646132    2.581645         -5          5
{txt}(2146 missing values generated)

disagree_av {c |}
 g_weight01 {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
          0 {c |}{res}        185        8.83        8.83
{txt}   .0333333 {c |}{res}        105        5.01       13.85
{txt}        .05 {c |}{res}          7        0.33       14.18
{txt}   .0666666 {c |}{res}        125        5.97       20.15
{txt}         .1 {c |}{res}        209        9.98       30.13
{txt}   .1333333 {c |}{res}         86        4.11       34.24
{txt}        .15 {c |}{res}          9        0.43       34.67
{txt}   .1666667 {c |}{res}         51        2.44       37.11
{txt}         .2 {c |}{res}         64        3.06       40.16
{txt}   .2333333 {c |}{res}         44        2.10       42.26
{txt}        .25 {c |}{res}          2        0.10       42.36
{txt}   .2666667 {c |}{res}         63        3.01       45.37
{txt}         .3 {c |}{res}        129        6.16       51.53
{txt}   .3333333 {c |}{res}        115        5.49       57.02
{txt}        .35 {c |}{res}          5        0.24       57.26
{txt}   .3666667 {c |}{res}         98        4.68       61.94
{txt}         .4 {c |}{res}         78        3.72       65.66
{txt}   .4333333 {c |}{res}         39        1.86       67.53
{txt}        .45 {c |}{res}         11        0.53       68.05
{txt}   .4666667 {c |}{res}         25        1.19       69.25
{txt}         .5 {c |}{res}         54        2.58       71.82
{txt}   .5333334 {c |}{res}         69        3.30       75.12
{txt}        .55 {c |}{res}          5        0.24       75.36
{txt}   .5666667 {c |}{res}         63        3.01       78.37
{txt}         .6 {c |}{res}         98        4.68       83.05
{txt}   .6333333 {c |}{res}         51        2.44       85.48
{txt}        .65 {c |}{res}          4        0.19       85.67
{txt}   .6666667 {c |}{res}         35        1.67       87.34
{txt}         .7 {c |}{res}         59        2.82       90.16
{txt}   .7333333 {c |}{res}         32        1.53       91.69
{txt}        .75 {c |}{res}          4        0.19       91.88
{txt}   .7666667 {c |}{res}         51        2.44       94.32
{txt}         .8 {c |}{res}         39        1.86       96.18
{txt}   .8333333 {c |}{res}         24        1.15       97.33
{txt}        .85 {c |}{res}          2        0.10       97.42
{txt}   .8666667 {c |}{res}         14        0.67       98.09
{txt}         .9 {c |}{res}         24        1.15       99.24
{txt}   .9333333 {c |}{res}          8        0.38       99.62
{txt}        .95 {c |}{res}          1        0.05       99.67
{txt}   .9666666 {c |}{res}          3        0.14       99.81
{txt}          1 {c |}{res}          4        0.19      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      2,094      100.00
{txt}
{com}.                         
.                         label var disagree_total_weight "Network Disagreement"
{txt}
{com}.                         label var disagree_total_weight "Network Disagreement"
{txt}
{com}.                 
.                         label var disagree_avg_weight "Network Disagreement"
{txt}
{com}.                         label var disagree_avg_weight "Network Disagreement"
{txt}
{com}.                 
.                 
.                 ***************************************************
.                 *****************Control Variables*****************
.                 ***************************************************
.         
. *Education
.         recode DER05 (-6=.) (-2=.) (5=4),  gen(educ)
{txt}(1616 differences between DER05 and educ)

{com}.         label var educ "Education"
{txt}
{com}.         label def ed 1 "< HS" 2 "HS" 3 " Some College" 4 "Bachelor+"
{txt}
{com}.         label values educ ed
{txt}
{com}.         
.         summ educ 

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 8}educ {c |}{res}      3222     3.16946    .8536997          1          4
{txt}
{com}.         gen educ01 = (educ - r(min))/(r(max)-r(min))
{txt}(1018 missing values generated)

{com}.         label var educ "Education"
{txt}
{com}.         
. *Age
.         rename DER02 age
{res}{txt}
{com}.         label var age "Age"
{txt}
{com}.         
.         summ age 

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 9}age {c |}{res}      4240    50.78184    15.78628         18         90
{txt}
{com}.         gen age01 = (age - r(min))/(r(max)-r(min))
{txt}
{com}.         label var age01 "Age"
{txt}
{com}.         
.         
. *Income
.         rename DER06 income
{res}{txt}
{com}.         replace income = . if income < 0        
{txt}(1053 real changes made, 1053 to missing)

{com}.         label var income "Income"
{txt}
{com}.         
.         summ income

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 6}income {c |}{res}      3187    12.22529    4.131404          1         19
{txt}
{com}.         gen income01 = (income - r(min))/(r(max)-r(min))
{txt}(1053 missing values generated)

{com}.         label var income01 "Income"
{txt}
{com}.         
.         
. *Race
.         rename DER04 race_eth
{res}{txt}
{com}.         label var race_eth "Race/Ethnicity"
{txt}
{com}.         label def rac 1 "White" 2 "Black" 3 "Hispanic" 4 "Other"
{txt}
{com}.         label values race_eth rac
{txt}
{com}.         
. *Gender
.         recode DER01 (1=0) (2=1), gen(gender)
{txt}(4240 differences between DER01 and gender)

{com}.         label var gender "Gender" 
{txt}
{com}.         label def gena 1 "Female" 0 "Male"
{txt}
{com}.         label values gender gena
{txt}
{com}. 
. *Employment
.         *CP (Core Profile?) Question
.                 recode CPQ17 (-7=.) (-6=.) (-5=.) (-4=.) (1=1) (2=1) (3=2) (4=2) (5=3) (6=4) (7=5), gen(empl_cp)
{txt}(2729 differences between CPQ17 and empl_cp)

{com}.                 label var empl_cp "Employment (CP)"
{txt}
{com}.                 label def emp 1 "Working" 2 "Unemployed" 3 "Retired" 4 "Disabled" 5 "Not Working: Other" 
{txt}
{com}.                 label values empl_cp emp
{txt}
{com}.                 
.                 recode empl_cp (1=1) (2=2) (3=3) (4=4) (5=4) , gen(empl_cp1)
{txt}(220 differences between empl_cp and empl_cp1)

{com}.                 label def emp2 1 "Working" 2 "Unemployed" 3 "Retired" 4 "Disabled/Not Working/Other"
{txt}
{com}.                 label values empl_cp1 emp2
{txt}
{com}.                 label var empl_cp1 "Employment"
{txt}
{com}. 
.                 
.         *Employment (W11)
.                 recode W11ZG1 (-7=.) (-6=.) (-5=.) (-4=.) (1=1) (2=1) (3=2) (4=2) (5=3) (6=4) (7=5), gen(empl_w11)
{txt}(2828 differences between W11ZG1 and empl_w11)

{com}.                 label var empl_w11 "Employment (W11)"
{txt}
{com}.                 label values empl_w11 emp
{txt}
{com}.                 
.                 recode empl_w11 (1=1) (2=2) (3=3)(4=4) (5=4) (6=4) (7=4), gen(empl_w11a)
{txt}(146 differences between empl_w11 and empl_w11a)

{com}.                 label values empl_w11a emp2
{txt}
{com}.                 label var empl_w11a "Employment (W11)"
{txt}
{com}.                 
.                 
.                 
.         *Relationship
.                 tab empl_cp empl_w11, row col
{txt}
{c TLC}{hline 19}{c TRC}
{c |} Key{col 21}{c |}
{c LT}{hline 19}{c RT}
{c |}{space 5}{it:frequency}{col 21}{c |}
{c |}{space 2}{it:row percentage}{col 21}{c |}
{c |}{space 1}{it:column percentage}{col 21}{c |}
{c BLC}{hline 19}{c BRC}

                   {c |}                    Employment (W11)
   Employment (CP) {c |}   Working  Unemploye    Retired   Disabled  Not Worki {c |}     Total
{hline 19}{c +}{hline 55}{c +}{hline 10}
           Working {c |}{res}     1,480         44         32          8         22 {txt}{c |}{res}     1,586 
                   {txt}{c |}{res}     93.32       2.77       2.02       0.50       1.39 {txt}{c |}{res}    100.00 
                   {txt}{c |}{res}     92.79      48.35       6.32       7.92      16.79 {txt}{c |}{res}     65.43 
{txt}{hline 19}{c +}{hline 55}{c +}{hline 10}
        Unemployed {c |}{res}        42         36          3          0          4 {txt}{c |}{res}        85 
                   {txt}{c |}{res}     49.41      42.35       3.53       0.00       4.71 {txt}{c |}{res}    100.00 
                   {txt}{c |}{res}      2.63      39.56       0.59       0.00       3.05 {txt}{c |}{res}      3.51 
{txt}{hline 19}{c +}{hline 55}{c +}{hline 10}
           Retired {c |}{res}        25          4        440          4          8 {txt}{c |}{res}       481 
                   {txt}{c |}{res}      5.20       0.83      91.48       0.83       1.66 {txt}{c |}{res}    100.00 
                   {txt}{c |}{res}      1.57       4.40      86.96       3.96       6.11 {txt}{c |}{res}     19.84 
{txt}{hline 19}{c +}{hline 55}{c +}{hline 10}
          Disabled {c |}{res}         3          0          9         85          0 {txt}{c |}{res}        97 
                   {txt}{c |}{res}      3.09       0.00       9.28      87.63       0.00 {txt}{c |}{res}    100.00 
                   {txt}{c |}{res}      0.19       0.00       1.78      84.16       0.00 {txt}{c |}{res}      4.00 
{txt}{hline 19}{c +}{hline 55}{c +}{hline 10}
Not Working: Other {c |}{res}        45          7         22          4         97 {txt}{c |}{res}       175 
                   {txt}{c |}{res}     25.71       4.00      12.57       2.29      55.43 {txt}{c |}{res}    100.00 
                   {txt}{c |}{res}      2.82       7.69       4.35       3.96      74.05 {txt}{c |}{res}      7.22 
{txt}{hline 19}{c +}{hline 55}{c +}{hline 10}
             Total {c |}{res}     1,595         91        506        101        131 {txt}{c |}{res}     2,424 
                   {txt}{c |}{res}     65.80       3.75      20.87       4.17       5.40 {txt}{c |}{res}    100.00 
                   {txt}{c |}{res}    100.00     100.00     100.00     100.00     100.00 {txt}{c |}{res}    100.00 

{txt}
{com}. 
. *Married Status
.         recode DER24 (-6=.) (-2=.) (1=1) (2=0) (3=0) (4=0) (5=0) (6=0) (7=0) (8=0) (9=0), gen(marital)
{txt}(2240 differences between DER24 and marital)

{com}.         label var marital "Marriage Status"
{txt}
{com}.         label def mar 1 "Married" 0 "Not Married" 
{txt}
{com}.         label values marital mar
{txt}
{com}.         tab marital

   {txt}Marriage {c |}
     Status {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
Not Married {c |}{res}      1,169       36.89       36.89
{txt}    Married {c |}{res}      2,000       63.11      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      3,169      100.00
{txt}
{com}. 
. *Interest
.         label def inte 1 "Not Interested" 2 "Slightly" 3 "Moderately" 4 "Very" 5 "Extremely"
{txt}
{com}.         *W1
.                 recode  W1K1 (-7=.) (-6=.) (-5=.) (-4=.) (1=5) (2=4) (3=3) (4=2) (5=1), gen(interest_w1)
{txt}(3738 differences between W1K1 and interest_w1)

{com}.                 label var interest_w1 "Pol. Interest (W1)"
{txt}
{com}.                 label values interest_w1 inte
{txt}
{com}.                 tab interest_w1

 {txt}Pol. Interest {c |}
          (W1) {c |}      Freq.     Percent        Cum.
{hline 15}{c +}{hline 35}
Not Interested {c |}{res}         42        2.60        2.60
{txt}      Slightly {c |}{res}        182       11.26       13.86
{txt}    Moderately {c |}{res}        502       31.06       44.93
{txt}          Very {c |}{res}        524       32.43       77.35
{txt}     Extremely {c |}{res}        366       22.65      100.00
{txt}{hline 15}{c +}{hline 35}
         Total {c |}{res}      1,616      100.00
{txt}
{com}.         *W9
.                 recode  W9H1 (-7=.) (-6=.) (-5=.) (-4=.) (1=5) (2=4) (3=3) (4=2) (5=1), gen(interest_w9)
{txt}(3441 differences between W9H1 and interest_w9)

{com}.                 label var interest_w9 "Pol. Interest (W9)"
{txt}
{com}.                 label values interest_w9 inte
{txt}
{com}.                 tab interest_w9

 {txt}Pol. Interest {c |}
          (W9) {c |}      Freq.     Percent        Cum.
{hline 15}{c +}{hline 35}
Not Interested {c |}{res}         52        1.90        1.90
{txt}      Slightly {c |}{res}        266        9.71       11.61
{txt}    Moderately {c |}{res}        799       29.17       40.78
{txt}          Very {c |}{res}        995       36.33       77.11
{txt}     Extremely {c |}{res}        627       22.89      100.00
{txt}{hline 15}{c +}{hline 35}
         Total {c |}{res}      2,739      100.00
{txt}
{com}.         *W10
.                 recode  W10H1 (-7=.) (-6=.) (-5=.) (-4=.) (1=5) (2=4) (3=3) (4=2) (5=1), gen(interest_w10)
{txt}(3476 differences between W10H1 and interest_w10)

{com}.                 label var interest_w10 "Pol. Interest (W10)"
{txt}
{com}.                 label values interest_w10 inte
{txt}
{com}.                 tab interest_w10

 {txt}Pol. Interest {c |}
         (W10) {c |}      Freq.     Percent        Cum.
{hline 15}{c +}{hline 35}
Not Interested {c |}{res}         51        1.89        1.89
{txt}      Slightly {c |}{res}        255        9.47       11.36
{txt}    Moderately {c |}{res}        764       28.37       39.73
{txt}          Very {c |}{res}        949       35.24       74.97
{txt}     Extremely {c |}{res}        674       25.03      100.00
{txt}{hline 15}{c +}{hline 35}
         Total {c |}{res}      2,693      100.00
{txt}
{com}.         *W11
.                         recode  W11H1 (-7=.) (-6=.) (-5=.) (-4=.) (1=5) (2=4) (3=3) (4=2) (5=1), gen(interest_w11)
{txt}(3548 differences between W11H1 and interest_w11)

{com}.                 label var interest_w11 "Pol. Interest (W11)"
{txt}
{com}.                 label values interest_w11 inte
{txt}
{com}.                 tab interest_w11

 {txt}Pol. Interest {c |}
         (W11) {c |}      Freq.     Percent        Cum.
{hline 15}{c +}{hline 35}
Not Interested {c |}{res}         56        2.10        2.10
{txt}      Slightly {c |}{res}        213        7.99       10.09
{txt}    Moderately {c |}{res}        692       25.97       36.06
{txt}          Very {c |}{res}      1,005       37.71       73.77
{txt}     Extremely {c |}{res}        699       26.23      100.00
{txt}{hline 15}{c +}{hline 35}
         Total {c |}{res}      2,665      100.00
{txt}
{com}.         *W19
.                 recode  W19H1 (-7=.) (-6=.) (-5=.) (-4=.) (1=5) (2=4) (3=3) (4=2) (5=1), gen(interest_w19)
{txt}(3389 differences between W19H1 and interest_w19)

{com}.                 label var interest_w19 "Pol. Interest (W19)"
{txt}
{com}.                 label values interest_w19 inte
{txt}
{com}.                 tab interest_w19

 {txt}Pol. Interest {c |}
         (W19) {c |}      Freq.     Percent        Cum.
{hline 15}{c +}{hline 35}
Not Interested {c |}{res}         68        2.85        2.85
{txt}      Slightly {c |}{res}        405       16.96       19.81
{txt}    Moderately {c |}{res}        851       35.64       55.44
{txt}          Very {c |}{res}        686       28.73       84.17
{txt}     Extremely {c |}{res}        378       15.83      100.00
{txt}{hline 15}{c +}{hline 35}
         Total {c |}{res}      2,388      100.00
{txt}
{com}.         *Relationship
.                 pwcorr interest_w1 interest_w9  interest_w10  interest_w11  interest_w19, sig 

             {txt}{c |} inter~w1 inter~w9 inter~10 inter~11 inter~19
{hline 13}{c +}{hline 45}
 interest_w1 {c |} {res}  1.0000 
             {txt}{c |}
             {c |}
 interest_w9 {c |} {res}  0.6684   1.0000 
             {txt}{c |}{res}   0.0000
             {txt}{c |}
interest_w10 {c |} {res}  0.6504   0.7191   1.0000 
             {txt}{c |}{res}   0.0000   0.0000
             {txt}{c |}
interest_w11 {c |} {res}  0.6436   0.6772   0.7086   1.0000 
             {txt}{c |}{res}   0.0000   0.0000   0.0000
             {txt}{c |}
interest_w19 {c |} {res}  0.6422   0.6546   0.6480   0.6311   1.0000 
             {txt}{c |}{res}   0.0000   0.0000   0.0000   0.0000
             {txt}{c |}

{com}.         
. 
. *Avg. Discussant Interest in Politics
.         label def discinterest 1 "Not at all" 2 "Slightly" 3 "Moderately" 4 "Very" 5 "Extremely"
{txt}
{com}.         gen interestd1 = .
{txt}(4240 missing values generated)

{com}.         replace interestd1 = 1 if W9ZD17_1 == 5
{txt}(47 real changes made)

{com}.         replace interestd1 = 2 if W9ZD17_1 == 4
{txt}(180 real changes made)

{com}.         replace interestd1 = 3 if W9ZD17_1 == 3
{txt}(440 real changes made)

{com}.         replace interestd1 = 4 if W9ZD17_1 == 2
{txt}(602 real changes made)

{com}.         replace interestd1 = 5 if W9ZD17_1 == 1         
{txt}(804 real changes made)

{com}.         label var interestd1 "Disc. 1 Interest Level"
{txt}
{com}.         label values interestd1 discinterest 
{txt}
{com}.         tab interestd1 W9ZD17_1         

   {txt}Disc. 1 {c |}          w9zd17_1. How interested is name1 in
  Interest {c |}                  government/politics
     Level {c |} 1. Extrem  2. Very i  3. Modera  4. Slight  5. Not in {c |}     Total
{hline 11}{c +}{hline 55}{c +}{hline 10}
Not at all {c |}{res}         0          0          0          0         47 {txt}{c |}{res}        47 
{txt}  Slightly {c |}{res}         0          0          0        180          0 {txt}{c |}{res}       180 
{txt}Moderately {c |}{res}         0          0        440          0          0 {txt}{c |}{res}       440 
{txt}      Very {c |}{res}         0        602          0          0          0 {txt}{c |}{res}       602 
{txt} Extremely {c |}{res}       804          0          0          0          0 {txt}{c |}{res}       804 
{txt}{hline 11}{c +}{hline 55}{c +}{hline 10}
     Total {c |}{res}       804        602        440        180         47 {txt}{c |}{res}     2,073 

{txt}
{com}.                                 
.         gen interestd2 = .
{txt}(4240 missing values generated)

{com}.         replace interestd2 = 1 if W9ZD17_2 == 5
{txt}(45 real changes made)

{com}.         replace interestd2 = 2 if W9ZD17_2 == 4
{txt}(199 real changes made)

{com}.         replace interestd2 = 3 if W9ZD17_2 == 3
{txt}(466 real changes made)

{com}.         replace interestd2 = 4 if W9ZD17_2 == 2
{txt}(612 real changes made)

{com}.         replace interestd2 = 5 if W9ZD17_2 == 1         
{txt}(645 real changes made)

{com}.         label var interestd2 "Disc. 2 Interest Level"
{txt}
{com}.         label values interestd2 discinterest                    
{txt}
{com}.         tab interestd2 W9ZD17_2         

   {txt}Disc. 2 {c |}          w9zd17_2. How interested is name2 in
  Interest {c |}                  government/politics
     Level {c |} 1. Extrem  2. Very i  3. Modera  4. Slight  5. Not in {c |}     Total
{hline 11}{c +}{hline 55}{c +}{hline 10}
Not at all {c |}{res}         0          0          0          0         45 {txt}{c |}{res}        45 
{txt}  Slightly {c |}{res}         0          0          0        199          0 {txt}{c |}{res}       199 
{txt}Moderately {c |}{res}         0          0        466          0          0 {txt}{c |}{res}       466 
{txt}      Very {c |}{res}         0        612          0          0          0 {txt}{c |}{res}       612 
{txt} Extremely {c |}{res}       645          0          0          0          0 {txt}{c |}{res}       645 
{txt}{hline 11}{c +}{hline 55}{c +}{hline 10}
     Total {c |}{res}       645        612        466        199         45 {txt}{c |}{res}     1,967 

{txt}
{com}. 
.         gen interestd3 = .
{txt}(4240 missing values generated)

{com}.         replace interestd3 = 1 if W9ZD17_3 == 5
{txt}(49 real changes made)

{com}.         replace interestd3 = 2 if W9ZD17_3 == 4
{txt}(214 real changes made)

{com}.         replace interestd3 = 3 if W9ZD17_3 == 3
{txt}(466 real changes made)

{com}.         replace interestd3 = 4 if W9ZD17_3 == 2
{txt}(565 real changes made)

{com}.         replace interestd3 = 5 if W9ZD17_3 == 1         
{txt}(550 real changes made)

{com}.         label var interestd3 "Disc. 3 Interest Level"
{txt}
{com}.         label values interestd3 discinterest 
{txt}
{com}.         tab interestd3 W9ZD17_3         

   {txt}Disc. 3 {c |}          w9zd17_3. How interested is name3 in
  Interest {c |}                  government/politics
     Level {c |} 1. Extrem  2. Very i  3. Modera  4. Slight  5. Not in {c |}     Total
{hline 11}{c +}{hline 55}{c +}{hline 10}
Not at all {c |}{res}         0          0          0          0         49 {txt}{c |}{res}        49 
{txt}  Slightly {c |}{res}         0          0          0        214          0 {txt}{c |}{res}       214 
{txt}Moderately {c |}{res}         0          0        466          0          0 {txt}{c |}{res}       466 
{txt}      Very {c |}{res}         0        565          0          0          0 {txt}{c |}{res}       565 
{txt} Extremely {c |}{res}       550          0          0          0          0 {txt}{c |}{res}       550 
{txt}{hline 11}{c +}{hline 55}{c +}{hline 10}
     Total {c |}{res}       550        565        466        214         49 {txt}{c |}{res}     1,844 

{txt}
{com}.         
.         summ interestd1-interestd3

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 2}interestd1 {c |}{res}      2073    3.933912    1.071288          1          5
{txt}{space 2}interestd2 {c |}{res}      1967    3.820031    1.069386          1          5
{txt}{space 2}interestd3 {c |}{res}      1844    3.733731    1.088155          1          5
{txt}
{com}.         
.         egen network_interest = rowmean(interestd1 interestd2 interestd3)
{txt}(2167 missing values generated)

{com}.         label var network_interest "Network Pol. Interest"
{txt}
{com}.         
.                 *Weighted Scale
.                 
.                         *Agree/Weight
.                         gen a1i = d1_agree * interestd1
{txt}(2170 missing values generated)

{com}.                         gen a2i = d2_agree * interestd2
{txt}(2277 missing values generated)

{com}.                         gen a3i = d3_agree * interestd3
{txt}(2400 missing values generated)

{com}.                         egen agree_int = rowtotal(a1i a2i a3i), missing
{txt}(2169 missing values generated)

{com}.                 
.                         *Disagree/Weight
.                         gen d1i = d1_disagree * interestd1
{txt}(2170 missing values generated)

{com}.                         gen d2i = d2_disagree * interestd2
{txt}(2277 missing values generated)

{com}.                         gen d3i = d3_disagree * interestd3
{txt}(2400 missing values generated)

{com}.                         egen disagree_int = rowtotal(d1i d2i d3i), missing
{txt}(2169 missing values generated)

{com}.                 
.                         *Scale
.                                 gen disagree_total_int = disagree_int - agree_int
{txt}(2169 missing values generated)

{com}.                                 gen disagree_avg_int = disagree_total_int/(pid_disagree + pid_agree)
{txt}(2169 missing values generated)

{com}.                                 label var disagree_total_int "Network Disagreement (Interest Weighted)"
{txt}
{com}.                                 label var disagree_avg_int "Network Disagreement (Interest Weighted)"
{txt}
{com}.                                 
.                                 foreach var in disagree_total_int disagree_avg_int {c -(}
{txt}  2{com}.                                         summ `var'
{txt}  3{com}.                                         gen `var'01 = (`var' - r(min))/(r(max)-r(min))
{txt}  4{com}.                                         {c )-}

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
disagr~l_int {c |}{res}      2071   -3.577982     7.96833        -15         15
{txt}(2169 missing values generated)

    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
disagr~g_int {c |}{res}      2071    -1.24489     2.80755         -5          5
{txt}(2169 missing values generated)

{com}.                                 
.                                 label var disagree_total_int01 "Network Disagreement (Interest Weighted)"
{txt}
{com}.                                 label var disagree_avg_int01 "Network Disagreement (Interest Weighted)"
{txt}
{com}.                                 
. 
. *Network Ties - How close to each other?
. 
. recode W9ZD8A W9ZD8B W9ZD8C (-6=.) (-5=.) (-1=.) ///
>         (1=6) (2=5) (3=4) (4=3) (5=2) (6=1), gen(tclose1 tclose2 tclose3)
{txt}(4240 differences between W9ZD8A and tclose1)
(4240 differences between W9ZD8B and tclose2)
(4240 differences between W9ZD8C and tclose3)

{com}. 
. egen network_close = rowmean(tclose1 tclose2 tclose3) 
{txt}(2223 missing values generated)

{com}. label var network_close "Avg. Closeness of Ties to Each Other"
{txt}
{com}. 
. *Weighted by Tie Closenes*
.         recode  W9ZD4_1  W9ZD4_2  W9ZD4_3 ///
>                 (-7=.) (-6=.) (-5=.) (1=5) (2=4) (3=3) (4=2) (5=1), gen(close1 close2 close3)
{txt}(3948 differences between W9ZD4_1 and close1)
(3834 differences between W9ZD4_2 and close2)
(3836 differences between W9ZD4_3 and close3)

{com}.         
.         *Agree/Weight
.                         gen a1c = d1_agree * close1
{txt}(2156 missing values generated)

{com}.                         gen a2c = d2_agree * close2
{txt}(2257 missing values generated)

{com}.                         gen a3c = d3_agree * close3
{txt}(2381 missing values generated)

{com}.                         egen agree_close = rowtotal(a1c a2c a3c), missing
{txt}(2145 missing values generated)

{com}.                 
.                 *Disagree/Weight
.                         gen d1c = d1_disagree * close1
{txt}(2156 missing values generated)

{com}.                         gen d2c = d2_disagree * close2
{txt}(2257 missing values generated)

{com}.                         gen d3c = d3_disagree * close3
{txt}(2381 missing values generated)

{com}.                         egen disagree_close = rowtotal(d1c d2c d3c), missing
{txt}(2145 missing values generated)

{com}.                 
.                         *Scale
.                                 gen disagree_total_close = disagree_close - agree_close
{txt}(2145 missing values generated)

{com}.                 
.                 
.                                 
.         
. *Personal Economic Situation (W19)
.         recode W19WX3 (-7=.) (-6=.) (-5=.) (1=5) (2=4) (3=3) (4=2) (5=1), gen(pers_finance)
{txt}(3556 differences between W19WX3 and pers_finance)

{com}.         label var pers_finance "Personal Economic Situation, Past 3mos"
{txt}
{com}.         label def pers 1 "Not at all Diff." 2 "Slightly Difficult" 3 "Moderately" 4 "Very" 5 "Extremely"
{txt}
{com}.         label values pers_finance pers
{txt}
{com}.         
.         
. *Political Knowledge
. 
. 
.         *W2
.                 label def correct 1 "Correct" 0 "Incorrect/NA"
{txt}
{com}.                 *Presidential Term Liimit*
.                         gen term = . 
{txt}(4240 missing values generated)

{com}.                         replace term = 1 if W2U2 == 2
{txt}(1351 real changes made)

{com}.                         replace term = 0 if W2U2 == 1
{txt}(27 real changes made)

{com}.                         replace term = 0 if W2U2 >=3 & W2U2 <= 60
{txt}(59 real changes made)

{com}.                         replace term = 0 if W2U2 == -7
{txt}(19 real changes made)

{com}.                         label var term "Pres. Term Limit" 
{txt}
{com}.                         label values term correct
{txt}
{com}.                         tab term        

  {txt}Pres. Term {c |}
       Limit {c |}      Freq.     Percent        Cum.
{hline 13}{c +}{hline 35}
Incorrect/NA {c |}{res}        105        7.21        7.21
{txt}     Correct {c |}{res}      1,351       92.79      100.00
{txt}{hline 13}{c +}{hline 35}
       Total {c |}{res}      1,456      100.00
{txt}
{com}.                 *Senator Term
.                         gen sen_term = . 
{txt}(4240 missing values generated)

{com}.                         replace sen_term = 1 if W2U3 == 6
{txt}(547 real changes made)

{com}.                         replace sen_term = 0 if W2U3 >=0 & W2U3 <= 5
{txt}(740 real changes made)

{com}.                         replace sen_term = 0 if W2U3 >=7 & W2U3 <=66
{txt}(119 real changes made)

{com}.                         replace sen_term = 0 if W2U3 == -7
{txt}(51 real changes made)

{com}.                         label var sen_term "Senator Term Limit"
{txt}
{com}.                         label values sen_term correct
{txt}
{com}.                         tab sen_term 

{txt}Senator Term {c |}
       Limit {c |}      Freq.     Percent        Cum.
{hline 13}{c +}{hline 35}
Incorrect/NA {c |}{res}        910       62.46       62.46
{txt}     Correct {c |}{res}        547       37.54      100.00
{txt}{hline 13}{c +}{hline 35}
       Total {c |}{res}      1,457      100.00
{txt}
{com}.                         
.                 *Senator Per State
.                         gen senators = .
{txt}(4240 missing values generated)

{com}.                         replace senators = 1 if W2U4 == 2
{txt}(1068 real changes made)

{com}.                         replace senators = 0 if W2U4 >=3 & W2U4 <= 1000
{txt}(131 real changes made)

{com}.                         replace senators = 0 if W2U4 >=0 & W2U4 <= 1
{txt}(181 real changes made)

{com}.                         replace senators = 0 if W2U4 == -7
{txt}(77 real changes made)

{com}.                         label var senators "#Senators per State"
{txt}
{com}.                         label values senators correct
{txt}
{com}.                         tab senators

   {txt}#Senators {c |}
   per State {c |}      Freq.     Percent        Cum.
{hline 13}{c +}{hline 35}
Incorrect/NA {c |}{res}        389       26.70       26.70
{txt}     Correct {c |}{res}      1,068       73.30      100.00
{txt}{hline 13}{c +}{hline 35}
       Total {c |}{res}      1,457      100.00
{txt}
{com}.                         
.                         
.                 *House Term
.                         gen house_term = .
{txt}(4240 missing values generated)

{com}.                         replace house_term = 1 if W2U5 == 2
{txt}(568 real changes made)

{com}.                         replace house_term = 0 if W2U5 >=0 & W2U5 <=1
{txt}(12 real changes made)

{com}.                         replace house_term = 0 if W2U5 >=3 & W2U5 <=100
{txt}(803 real changes made)

{com}.                         replace house_term = 0 if W2U5 == -7
{txt}(74 real changes made)

{com}.                         label var house_term "House Rep Term Limit"
{txt}
{com}.                         label values house_term correct
{txt}
{com}.                         tab house_term

   {txt}House Rep {c |}
  Term Limit {c |}      Freq.     Percent        Cum.
{hline 13}{c +}{hline 35}
Incorrect/NA {c |}{res}        889       61.02       61.02
{txt}     Correct {c |}{res}        568       38.98      100.00
{txt}{hline 13}{c +}{hline 35}
       Total {c |}{res}      1,457      100.00
{txt}
{com}.                         
.                 
.                 *Who Takes Over if Pres and VP Die
.                         gen succession = . 
{txt}(4240 missing values generated)

{com}.                         replace succession = 1 if W2U6 == 3
{txt}(994 real changes made)

{com}.                         replace succession = 0 if W2U6 >=1 & W2U6 <=2
{txt}(425 real changes made)

{com}.                         replace succession = 0 if W2U6 == -7
{txt}(38 real changes made)

{com}.                         label var succession "Pres Succession"
{txt}
{com}.                         label values succession correct
{txt}
{com}.                         tab succession

        {txt}Pres {c |}
  Succession {c |}      Freq.     Percent        Cum.
{hline 13}{c +}{hline 35}
Incorrect/NA {c |}{res}        463       31.78       31.78
{txt}     Correct {c |}{res}        994       68.22      100.00
{txt}{hline 13}{c +}{hline 35}
       Total {c |}{res}      1,457      100.00
{txt}
{com}.                 
.                 *Overriding a Veto
.                         gen veto = . 
{txt}(4240 missing values generated)

{com}.                         replace veto = 1 if W2U7 == 2
{txt}(1040 real changes made)

{com}.                         replace veto = 0 if W2U7 == 1
{txt}(114 real changes made)

{com}.                         replace veto = 0 if W2U7 >=3 & W2U7 <=4
{txt}(264 real changes made)

{com}.                         replace veto = 0 if W2U7 == -7
{txt}(39 real changes made)

{com}.                         label var veto "Veto Override"
{txt}
{com}.                         label values veto correct
{txt}
{com}.                         tab veto

        {txt}Veto {c |}
    Override {c |}      Freq.     Percent        Cum.
{hline 13}{c +}{hline 35}
Incorrect/NA {c |}{res}        417       28.62       28.62
{txt}     Correct {c |}{res}      1,040       71.38      100.00
{txt}{hline 13}{c +}{hline 35}
       Total {c |}{res}      1,457      100.00
{txt}
{com}.                 
.                 alpha term sen_term senators house_term succession veto, item

{txt}Test scale = mean(unstandardized items)

                                                            average
                             item-test     item-rest       interitem
Item         {c |}  Obs  Sign   correlation   correlation     covariance      alpha
{hline 13}{c +}{hline 65}
term{col 14}{c |}{res}{col 16}1456{col 24}+{col 31} 0.3857{col 45} 0.2279{col 59} .0498231{col 73} 0.5970
{txt}sen_term{col 14}{c |}{res}{col 16}1457{col 24}+{col 31} 0.6468{col 45} 0.3961{col 59} .0340593{col 73} 0.5310
{txt}senators{col 14}{c |}{res}{col 16}1457{col 24}+{col 31} 0.6396{col 45} 0.4136{col 59} .0348055{col 73} 0.5251
{txt}house_term{col 14}{c |}{res}{col 16}1457{col 24}+{col 31} 0.6078{col 45} 0.3406{col 59} .0369582{col 73} 0.5572
{txt}succession{col 14}{c |}{res}{col 16}1457{col 24}+{col 31} 0.6001{col 45} 0.3456{col 59} .0374132{col 73} 0.5540
{txt}veto{col 14}{c |}{res}{col 16}1457{col 24}+{col 31} 0.5577{col 45} 0.3000{col 59} .0403401{col 73} 0.5732
{txt}{hline 13}{c +}{hline 65}
Test scale{col 14}{c |}{res}{col 59} .0389004{col 73} 0.6026
{txt}{hline 13}{c BT}{hline 65}

{com}.                 egen knowl = rowtotal(term sen_term senators house_term succession veto), missing
{txt}(2783 missing values generated)

{com}.                 
.                 summ knowl

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 7}knowl {c |}{res}      1457    3.821551    1.524886          0          6
{txt}
{com}.                 gen knowl01 = (knowl - r(min))/(r(max)-r(min))
{txt}(2783 missing values generated)

{com}.                 label var knowl01 "Knowledge" 
{txt}
{com}.                 
.                 
.         
. ***Partisan Ambivalence***
. 
. 
.         *Wave 2
.                 *Fav: Dem
.                         gen dem_fav_w2 = . 
{txt}(4240 missing values generated)

{com}.                         replace dem_fav_w2 = 0 if W2L2 == 2
{txt}(534 real changes made)

{com}.                         replace dem_fav_w2 = 0.25  if W2L2 == 1 & W2L3 == 4
{txt}(213 real changes made)

{com}.                         replace dem_fav_w2 = 0.50  if W2L2 == 1 & W2L3 == 3
{txt}(353 real changes made)

{com}.                         replace dem_fav_w2 = 0.75  if W2L2 == 1 & W2L3 == 2
{txt}(246 real changes made)

{com}.                         replace dem_fav_w2 = 1  if W2L2 == 1 & W2L3 == 1
{txt}(97 real changes made)

{com}. 
.                 *Unfav: Dem
.                         gen dem_unfav_w2 = .
{txt}(4240 missing values generated)

{com}.                         replace dem_unfav_w2 = 0 if W2L4 == 2
{txt}(529 real changes made)

{com}.                         replace dem_unfav_w2 = 0.25  if W2L4 == 1 & W2L5 == 4
{txt}(286 real changes made)

{com}.                         replace dem_unfav_w2 = 0.50  if W2L4 == 1 & W2L5 == 3
{txt}(322 real changes made)

{com}.                         replace dem_unfav_w2 = 0.75  if W2L4 == 1 & W2L5 == 2
{txt}(185 real changes made)

{com}.                         replace dem_unfav_w2 = 1  if W2L4 == 1 & W2L5 == 1
{txt}(122 real changes made)

{com}.                 
.                 *Rep:Fav
.                         gen rep_fav_w2 = . 
{txt}(4240 missing values generated)

{com}.                         replace rep_fav_w2 = 0 if W2L7 == 2
{txt}(660 real changes made)

{com}.                         replace rep_fav_w2 = 0.25  if W2L7 == 1 & W2L8 == 4
{txt}(211 real changes made)

{com}.                         replace rep_fav_w2 = 0.50  if W2L7 == 1 & W2L8 == 3
{txt}(338 real changes made)

{com}.                         replace rep_fav_w2 = 0.75  if W2L7 == 1 & W2L8 == 2
{txt}(189 real changes made)

{com}.                         replace rep_fav_w2 = 1  if W2L7 == 1 & W2L8 == 1
{txt}(47 real changes made)

{com}.                 
.                 
.                 *Rep:Unfav
.                         gen rep_unfav_w2 = .
{txt}(4240 missing values generated)

{com}.                         replace rep_unfav_w2 = 0 if W2L9 == 2
{txt}(437 real changes made)

{com}.                         replace rep_unfav_w2 = 0.25  if W2L9 == 1 & W2L10 == 4
{txt}(274 real changes made)

{com}.                         replace rep_unfav_w2 = 0.50  if W2L9 == 1 & W2L10 == 3
{txt}(365 real changes made)

{com}.                         replace rep_unfav_w2 = 0.75  if W2L9 == 1 & W2L10 == 2
{txt}(216 real changes made)

{com}.                         replace rep_unfav_w2 = 1  if W2L9 == 1 & W2L10 == 1
{txt}(150 real changes made)

{com}.                 
.                 *ID Consistent
.                         gen consistent_w2 = .
{txt}(4240 missing values generated)

{com}.                         replace consistent_w2 = dem_fav_w2 + rep_unfav_w2 if pid_31 == 1
{txt}(628 real changes made)

{com}.                         replace consistent_w2 = dem_unfav_w2 + rep_fav_w2 if pid_31 == 2
{txt}(586 real changes made)

{com}.                         label var consistent_w2 "Partisan Identity Consistent Likes/Dislikes (W2)"
{txt}
{com}.                         
.                 *ID Conflicting
.                         gen conflicting_w2 = .
{txt}(4240 missing values generated)

{com}.                         replace conflicting_w2 = dem_unfav_w2 + rep_fav_w2 if pid_31 == 1
{txt}(628 real changes made)

{com}.                         replace conflicting_w2 = dem_fav_w2 + rep_unfav_w2 if pid_31 == 2
{txt}(583 real changes made)

{com}.                         label var conflicting_w2 "Partisan Identity Conflicting Likes/Dislikes (W2)"
{txt}
{com}.                         
.                 *In vs. out Fav/unfav
. 
.                         gen in_like_w2 = .
{txt}(4240 missing values generated)

{com}.                         replace in_like_w2  = dem_fav_w2 if pid_31 == 1
{txt}(628 real changes made)

{com}.                         replace in_like_w2 =  rep_fav_w2 if pid_31 == 2
{txt}(586 real changes made)

{com}.                 
.                         gen in_dislike_w2 = .
{txt}(4240 missing values generated)

{com}.                         replace in_dislike_w2 = dem_unfav_w2 if pid_31 == 1
{txt}(628 real changes made)

{com}.                         replace in_dislike_w2 = rep_unfav_w2 if pid_31 == 2
{txt}(583 real changes made)

{com}.                         
.                         gen out_like_w2 = .
{txt}(4240 missing values generated)

{com}.                         replace out_like_w2 = rep_fav_w2 if pid_31 == 1
{txt}(629 real changes made)

{com}.                         replace out_like_w2 = dem_fav_w2 if pid_31 == 2
{txt}(585 real changes made)

{com}.                         
.                         gen out_dislike_w2 = .
{txt}(4240 missing values generated)

{com}.                         replace out_dislike_w2 = rep_unfav_w2 if pid_31 == 1
{txt}(629 real changes made)

{com}.                         replace out_dislike_w2 = dem_unfav_w2 if pid_31 == 2
{txt}(586 real changes made)

{com}.                         
.                         recode in_like_w2 in_dislike_w2 out_like_w2 out_dislike_w2      (0 = 1) (0.25 = 2) (0.5 = 3) (0.75 =4) (1 = 5)
{txt}(in_like_w2: 1214 changes made)
(in_dislike_w2: 1211 changes made)
(out_like_w2: 1214 changes made)
(out_dislike_w2: 1215 changes made)

{com}.                         
.                         label var in_like_w2 "In-Party: Favorable (W2)"
{txt}
{com}.                         label var in_dislike_w2 "In-Party: Unfavorable (W2)"
{txt}
{com}.                         label var out_like_w2 "Out_Party: Favorable (W2)"
{txt}
{com}.                         label var out_dislike_w2 "Out_Party: Unfavorable (W2)"                  
{txt}
{com}.                                 
.                         label def fav 1 "No Fav. Thoughts" 2 "Slightly Favorable" 3 "Moderately Favorable" 4 "Very Favorable" 5 "Extremely Favorable"
{txt}
{com}.                         label values in_like_w2 out_like_w2 fav
{txt}
{com}.                         label def unfav 1 "No Unfav. Thoughts" 2 "Slightly Unfavorable" 3 "Moderately Unfavorable" 4 "Very Unfavorable" 5 "Extremely Unfavorable"
{txt}
{com}.                         label values in_dislike_w2 out_dislike_w2 unfav
{txt}
{com}.                         
.                         
.                         
.                         
. *W11                    
.                 *Fav: Dem
.                         gen dem_fav_w11 = . 
{txt}(4240 missing values generated)

{com}.                         replace dem_fav_w11 = 0 if W11LB2 == 2
{txt}(941 real changes made)

{com}.                         replace dem_fav_w11 = 0.25  if W11LB2 == 1 & W11LB4 == 4
{txt}(351 real changes made)

{com}.                         replace dem_fav_w11 = 0.50  if W11LB2 == 1 & W11LB4 == 3
{txt}(651 real changes made)

{com}.                         replace dem_fav_w11 = 0.75  if W11LB2 == 1 & W11LB4 == 2
{txt}(539 real changes made)

{com}.                         replace dem_fav_w11 = 1  if W11LB2 == 1 & W11LB4 == 1
{txt}(177 real changes made)

{com}. 
.                 *Unfav: Dem
.                         gen dem_unfav_w11 = .
{txt}(4240 missing values generated)

{com}.                         replace dem_unfav_w11 = 0 if W11LB3 == 2
{txt}(1086 real changes made)

{com}.                         replace dem_unfav_w11 = 0.25  if W11LB3 == 1 & W11LB5 == 4
{txt}(455 real changes made)

{com}.                         replace dem_unfav_w11 = 0.50  if W11LB3 == 1 & W11LB5 == 3
{txt}(582 real changes made)

{com}.                         replace dem_unfav_w11 = 0.75  if W11LB3 == 1 & W11LB5 == 2
{txt}(337 real changes made)

{com}.                         replace dem_unfav_w11 = 1  if W11LB3 == 1 & W11LB5 == 1
{txt}(201 real changes made)

{com}.                 
.                 *Rep:Fav
.                         gen rep_fav_w11 = . 
{txt}(4240 missing values generated)

{com}.                         replace rep_fav_w11 = 0 if W11LB7 == 2
{txt}(1178 real changes made)

{com}.                         replace rep_fav_w11 = 0.25  if W11LB7 == 1 & W11LB9 == 4
{txt}(377 real changes made)

{com}.                         replace rep_fav_w11 = 0.50  if W11LB7 == 1 & W11LB9 == 3
{txt}(661 real changes made)

{com}.                         replace rep_fav_w11 = 0.75  if W11LB7 == 1 & W11LB9 == 2
{txt}(367 real changes made)

{com}.                         replace rep_fav_w11 = 1  if W11LB7 == 1 & W11LB9 == 1
{txt}(80 real changes made)

{com}.                 
.                 
.                 *Rep:Unfav
.                         gen rep_unfav_w11 = .
{txt}(4240 missing values generated)

{com}.                         replace rep_unfav_w11 = 0 if W11LB8 == 2
{txt}(825 real changes made)

{com}.                         replace rep_unfav_w11 = 0.25  if W11LB8 == 1 & W11LB10 == 4
{txt}(281 real changes made)

{com}.                         replace rep_unfav_w11 = 0.50  if W11LB8 == 1 & W11LB10 == 3
{txt}(642 real changes made)

{com}.                         replace rep_unfav_w11 = 0.75  if W11LB8 == 1 & W11LB10 == 2
{txt}(464 real changes made)

{com}.                         replace rep_unfav_w11 = 1  if W11LB8 == 1 & W11LB10 == 1
{txt}(215 real changes made)

{com}.                 
.                 *ID Consistent
.                         gen consistent_w11 = .
{txt}(4240 missing values generated)

{com}.                         replace consistent_w11 = dem_fav_w11 + rep_unfav_w11 if pid_39 == 1
{txt}(1112 real changes made)

{com}.                         replace consistent_w11 = dem_unfav_w11 + rep_fav_w11 if pid_39 == 2
{txt}(1046 real changes made)

{com}.                         label var consistent_w11 "Partisan Identity Consistent Likes/Dislikes (W10)"
{txt}
{com}.                         
.                 *ID Conflicting
.                         gen conflicting_w11 = .
{txt}(4240 missing values generated)

{com}.                         replace conflicting_w11 = dem_unfav_w11 + rep_fav_w11 if pid_39 == 1
{txt}(1127 real changes made)

{com}.                         replace conflicting_w11 = dem_fav_w11 + rep_unfav_w11 if pid_39 == 2
{txt}(851 real changes made)

{com}.                         label var conflicting_w11 "Partisan Identity Conclifting Likes/Dislikes (W10)"
{txt}
{com}.                         
.                         *Earlier PID 
.                                         *ID Consistent
.                                         gen consistent_w11a = .
{txt}(4240 missing values generated)

{com}.                                         replace consistent_w11a = dem_fav_w11 + rep_unfav_w11 if pid_31 == 1
{txt}(607 real changes made)

{com}.                                         replace consistent_w11a = dem_unfav_w11 + rep_fav_w11 if pid_31 == 2
{txt}(595 real changes made)

{com}.                                         label var consistent_w11a "Partisan Identity Consistent Likes/Dislikes (W10)"
{txt}
{com}.                                         
.                                 *ID Conflicting
.                                         gen conflicting_w11a = .
{txt}(4240 missing values generated)

{com}.                                         replace conflicting_w11a = dem_unfav_w11 + rep_fav_w11 if pid_31 == 1
{txt}(614 real changes made)

{com}.                                         replace conflicting_w11a = dem_fav_w11 + rep_unfav_w11 if pid_31 == 2
{txt}(491 real changes made)

{com}.                                         label var conflicting_w11a "Partisan Identity Conclifting Likes/Dislikes (W10)"
{txt}
{com}.                 
.                                 
.         
.                         gen in_like_w11 = .
{txt}(4240 missing values generated)

{com}.                         replace in_like_w11  = dem_fav_w11 if pid_31 == 1
{txt}(614 real changes made)

{com}.                         replace in_like_w11 =  rep_fav_w11 if pid_31 == 2
{txt}(596 real changes made)

{com}.                 
.                         gen in_dislike_w11 = .
{txt}(4240 missing values generated)

{com}.                         replace in_dislike_w11 = dem_unfav_w11 if pid_31 == 1
{txt}(614 real changes made)

{com}.                         replace in_dislike_w11 = rep_unfav_w11 if pid_31 == 2
{txt}(494 real changes made)

{com}.                         
.                         gen out_like_w11 = .
{txt}(4240 missing values generated)

{com}.                         replace out_like_w11 = rep_fav_w11 if pid_31 == 1
{txt}(615 real changes made)

{com}.                         replace out_like_w11 = dem_fav_w11 if pid_31 == 2
{txt}(593 real changes made)

{com}.                         
.                         gen out_dislike_w11 = .
{txt}(4240 missing values generated)

{com}.                         replace out_dislike_w11 = rep_unfav_w11 if pid_31 == 1
{txt}(607 real changes made)

{com}.                         replace out_dislike_w11 = dem_unfav_w11 if pid_31 == 2
{txt}(595 real changes made)

{com}.                         
.                         label var in_like_w11 "In-Party: Favorable (W11)"
{txt}
{com}.                         label var in_dislike_w11 "In-Party: Unfavorable (W11)"
{txt}
{com}.                         label var out_like_w11 "Out_Party: Favorable (W11)"
{txt}
{com}.                         label var out_dislike_w11 "Out_Party: Unfavorable (W11)"                        
{txt}
{com}.                                 
.                         recode in_like_w11 in_dislike_w11 out_like_w11 out_dislike_w11 ///
>                                 (0 = 1) (0.25 = 2) (0.5 = 3) (0.75 =4) (1 = 5)
{txt}(in_like_w11: 1210 changes made)
(in_dislike_w11: 1108 changes made)
(out_like_w11: 1208 changes made)
(out_dislike_w11: 1202 changes made)

{com}.                         
.                         label values in_like_w11 out_like_w11 fav
{txt}
{com}.                         label values in_dislike_w11 out_dislike_w11 unfav
{txt}
{com}.                         
.                         
.                         
.                                 
.                         
. *Matching
. 
. gen black = .
{txt}(4240 missing values generated)

{com}. replace black = 1 if race == 2
{txt}(492 real changes made)

{com}. replace black = 0 if race == 1
{txt}(3292 real changes made)

{com}. replace black = 0 if race >=3 & race <=4
{txt}(456 real changes made)

{com}. 
. tabulate pid_str1, gen(pstr1_)

    {txt}PID Str {c |}
       (W1) {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
       Lean {c |}{res}        321       22.70       22.70
{txt} Not Strong {c |}{res}        489       34.58       57.28
{txt}     Strong {c |}{res}        604       42.72      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}      1,414      100.00
{txt}
{com}. 
. 
. recode DER09W1 (-7=.) (-4=.) (-6=.) (-5=.) ///
>         (1=4) (2=3) (3=2) (4=1) ///
>         (7=4) (6=3) (5=2), gen(ideol_str1)
{txt}(4240 differences between DER09W1 and ideol_str1)

{com}.         
. recode DER09W2 (-7=.) (-6=.) (-5=.) ///
>         (1=4) (2=3) (3=2) (4=1) ///
>         (7=4) (6=3) (5=2), gen(ideol_str2)
{txt}(4240 differences between DER09W2 and ideol_str2)

{com}. 
. summ interest_w9

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 1}interest_w9 {c |}{res}      2739    3.686017    .9908225          1          5
{txt}
{com}. gen interest_w901 = (interest_w9 - r(min))/(r(max)-r(min))
{txt}(1501 missing values generated)

{com}. label var interest_w901 "Political Interest" 
{txt}
{com}. 
. 
. **Need for cognition and need to evaluate***
. 
. recode W11ZE1 (1=4) (2=3) (3=2) (4=1) (-5 -6 -7 =.), gen(opinionated)
{txt}(4240 differences between W11ZE1 and opinionated)

{com}. 
. gen opinion_degree = . 
{txt}(4240 missing values generated)

{com}. replace opinion_degree = 1  if W11ZE2 == 1 & W11ZE3B == 1
{txt}(46 real changes made)

{com}. replace opinion_degree = 2  if W11ZE2 == 1 & W11ZE3B == 2
{txt}(263 real changes made)

{com}. replace opinion_degree = 3  if W11ZE2 == 2
{txt}(1684 real changes made)

{com}. replace opinion_degree = 4  if W11ZE2 == 3 & W11ZE3A == 2
{txt}(373 real changes made)

{com}. replace opinion_degree = 5  if W11ZE2 == 3 & W11ZE3A == 1
{txt}(292 real changes made)

{com}. 
. foreach var in opinionated opinion_degree {c -(}
{txt}  2{com}.         summ `var'
{txt}  3{com}.         gen `var'01 = (`var' - r(min))/(r(max)-r(min))
{txt}  4{com}.         {c )-}

{txt}    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
{space 1}opinionated {c |}{res}      2662    2.802404    .7817114          1          4
{txt}(1578 missing values generated)

    Variable {c |}       Obs        Mean    Std. Dev.       Min        Max
{hline 13}{c +}{hline 56}
opinion_de~e {c |}{res}      2658    3.226486    .8348036          1          5
{txt}(1582 missing values generated)

{com}. 
. egen evaluate = rowmean(opinionated01 opinion_degree01)
{txt}(1578 missing values generated)

{com}. egen evaluate1 = rowtotal(opinionated01 opinion_degree01), missing
{txt}(1578 missing values generated)

{com}. 
. label var evaluate1 "Need to Evaluate"
{txt}
{com}. 
. 
. recode W11ZE6 (1=0) (2=1) (-5 -6 -7 =.), gen(complex)
{txt}(4240 differences between W11ZE6 and complex)

{com}. 
. gen thinking = . 
{txt}(4240 missing values generated)

{com}. replace thinking = 1  if W11ZE4 == 2 & W11ZE5B == 1
{txt}(86 real changes made)

{com}. replace thinking = 2  if W11ZE4 == 2 & W11ZE5B == 2
{txt}(141 real changes made)

{com}. replace thinking = 3  if W11ZE4 == 3
{txt}(1092 real changes made)

{com}. replace thinking = 4  if W11ZE4 == 1 & W11ZE5A == 2
{txt}(512 real changes made)

{com}. replace thinking = 5  if W11ZE4 == 1 & W11ZE5A == 1
{txt}(830 real changes made)

{com}. 
. recode thinking (1=0) (2=0.25) (3=0.5) (4=0.75) (5=1), gen(thinking01)
{txt}(2661 differences between thinking and thinking01)

{com}. 
. egen nfc = rowmean(thinking01 complex)
{txt}(1578 missing values generated)

{com}. egen nfc1 = rowtotal(thinking01 complex), missing
{txt}(1578 missing values generated)

{com}. 
. label var nfc1 "Need for Cognition"
{txt}
{com}. 
. 
. 
. 
{txt}end of do-file

{com}. set more off
{txt}
{com}. 
. /***Partisan Extremity***/
. *W10*
. eststo clear
{txt}
{com}. foreach var in disagree_total disagree_avg disagree_total_int disagree_total_close  ///
>         disagree_total_weight gendiff  {c -(}
{txt}  2{com}.         eststo: ologit pid_str10_full `var' numgiven1 network_interest  i.pid_21 interest_w1 i.gender ///
>                         i.race age educ income i.marital evaluate1 nfc1 [pweight= WGTC10]
{txt}  3{com}.         {c )-}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -870.1663}  
Iteration 1:{space 3}log pseudolikelihood = {res:-809.01793}  
Iteration 2:{space 3}log pseudolikelihood = {res: -808.1389}  
Iteration 3:{space 3}log pseudolikelihood = {res:-808.13682}  
Iteration 4:{space 3}log pseudolikelihood = {res:-808.13682}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       758
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     47.07
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-808.13682{txt}{col 51}Pseudo R2{col 67}= {res}    0.0713

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}  pid_str10_full{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}disagree_total {c |}{col 18}{res}{space 2}-.2859017{col 30}{space 2} .0638919{col 41}{space 1}   -4.47{col 50}{space 3}0.000{col 58}{space 4}-.4111276{col 71}{space 3}-.1606759
{txt}{space 7}numgiven1 {c |}{col 18}{res}{space 2} .0012647{col 30}{space 2} .1861133{col 41}{space 1}    0.01{col 50}{space 3}0.995{col 58}{space 4}-.3635105{col 71}{space 3}   .36604
{txt}network_interest {c |}{col 18}{res}{space 2} .1530667{col 30}{space 2} .1121211{col 41}{space 1}    1.37{col 50}{space 3}0.172{col 58}{space 4}-.0666866{col 71}{space 3} .3728201
{txt}{space 16} {c |}
{space 10}pid_21 {c |}
{space 7}Democrat  {c |}{col 18}{res}{space 2}  .038385{col 30}{space 2} .2109771{col 41}{space 1}    0.18{col 50}{space 3}0.856{col 58}{space 4}-.3751225{col 71}{space 3} .4518926
{txt}{space 5}interest_w1 {c |}{col 18}{res}{space 2}  .097966{col 30}{space 2} .1359085{col 41}{space 1}    0.72{col 50}{space 3}0.471{col 58}{space 4}-.1684098{col 71}{space 3} .3643418
{txt}{space 16} {c |}
{space 10}gender {c |}
{space 9}Female  {c |}{col 18}{res}{space 2} .5860516{col 30}{space 2} .2250497{col 41}{space 1}    2.60{col 50}{space 3}0.009{col 58}{space 4} .1449622{col 71}{space 3} 1.027141
{txt}{space 16} {c |}
{space 8}race_eth {c |}
{space 10}Black  {c |}{col 18}{res}{space 2} .7830491{col 30}{space 2} .6293882{col 41}{space 1}    1.24{col 50}{space 3}0.213{col 58}{space 4}-.4505291{col 71}{space 3} 2.016627
{txt}{space 7}Hispanic  {c |}{col 18}{res}{space 2}-.3525096{col 30}{space 2}   .44935{col 41}{space 1}   -0.78{col 50}{space 3}0.433{col 58}{space 4}-1.233219{col 71}{space 3} .5282002
{txt}{space 10}Other  {c |}{col 18}{res}{space 2}-.8471303{col 30}{space 2}   1.0627{col 41}{space 1}   -0.80{col 50}{space 3}0.425{col 58}{space 4}-2.929984{col 71}{space 3} 1.235723
{txt}{space 16} {c |}
{space 13}age {c |}{col 18}{res}{space 2} .0007245{col 30}{space 2} .0082543{col 41}{space 1}    0.09{col 50}{space 3}0.930{col 58}{space 4}-.0154535{col 71}{space 3} .0169026
{txt}{space 12}educ {c |}{col 18}{res}{space 2} .0548462{col 30}{space 2} .1253418{col 41}{space 1}    0.44{col 50}{space 3}0.662{col 58}{space 4}-.1908192{col 71}{space 3} .3005116
{txt}{space 10}income {c |}{col 18}{res}{space 2} .0000719{col 30}{space 2} .0327123{col 41}{space 1}    0.00{col 50}{space 3}0.998{col 58}{space 4}-.0640429{col 71}{space 3} .0641868
{txt}{space 16} {c |}
{space 9}marital {c |}
{space 8}Married  {c |}{col 18}{res}{space 2} .0451851{col 30}{space 2} .2948077{col 41}{space 1}    0.15{col 50}{space 3}0.878{col 58}{space 4}-.5326273{col 71}{space 3} .6229976
{txt}{space 7}evaluate1 {c |}{col 18}{res}{space 2} .1030042{col 30}{space 2} .3386141{col 41}{space 1}    0.30{col 50}{space 3}0.761{col 58}{space 4}-.5606673{col 71}{space 3} .7666757
{txt}{space 12}nfc1 {c |}{col 18}{res}{space 2}-.2380257{col 30}{space 2} .1709839{col 41}{space 1}   -1.39{col 50}{space 3}0.164{col 58}{space 4} -.573148{col 71}{space 3} .0970965
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
           /cut1 {c |}{col 18}{res}{space 2}-1.286547{col 30}{space 2} .9328031{col 58}{space 4}-3.114807{col 71}{space 3} .5417139
{txt}           /cut2 {c |}{col 18}{res}{space 2} .4611554{col 30}{space 2} .8805043{col 58}{space 4}-1.264601{col 71}{space 3} 2.186912
{txt}           /cut3 {c |}{col 18}{res}{space 2}  2.04594{col 30}{space 2} .9033893{col 58}{space 4} .2753297{col 71}{space 3} 3.816551
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -870.1663}  
Iteration 1:{space 3}log pseudolikelihood = {res:-807.86651}  
Iteration 2:{space 3}log pseudolikelihood = {res:-807.00755}  
Iteration 3:{space 3}log pseudolikelihood = {res:-807.00548}  
Iteration 4:{space 3}log pseudolikelihood = {res:-807.00548}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       758
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     51.44
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-807.00548{txt}{col 51}Pseudo R2{col 67}= {res}    0.0726

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}  pid_str10_full{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}disagree_avg {c |}{col 18}{res}{space 2}-.7922444{col 30}{space 2} .1710131{col 41}{space 1}   -4.63{col 50}{space 3}0.000{col 58}{space 4}-1.127424{col 71}{space 3}-.4570649
{txt}{space 7}numgiven1 {c |}{col 18}{res}{space 2} .0304708{col 30}{space 2}   .19491{col 41}{space 1}    0.16{col 50}{space 3}0.876{col 58}{space 4}-.3515458{col 71}{space 3} .4124875
{txt}network_interest {c |}{col 18}{res}{space 2} .1453315{col 30}{space 2} .1118386{col 41}{space 1}    1.30{col 50}{space 3}0.194{col 58}{space 4}-.0738682{col 71}{space 3} .3645312
{txt}{space 16} {c |}
{space 10}pid_21 {c |}
{space 7}Democrat  {c |}{col 18}{res}{space 2} .0159674{col 30}{space 2} .2130341{col 41}{space 1}    0.07{col 50}{space 3}0.940{col 58}{space 4}-.4015717{col 71}{space 3} .4335065
{txt}{space 5}interest_w1 {c |}{col 18}{res}{space 2} .1019023{col 30}{space 2} .1344622{col 41}{space 1}    0.76{col 50}{space 3}0.449{col 58}{space 4}-.1616387{col 71}{space 3} .3654433
{txt}{space 16} {c |}
{space 10}gender {c |}
{space 9}Female  {c |}{col 18}{res}{space 2} .5789417{col 30}{space 2} .2251178{col 41}{space 1}    2.57{col 50}{space 3}0.010{col 58}{space 4}  .137719{col 71}{space 3} 1.020164
{txt}{space 16} {c |}
{space 8}race_eth {c |}
{space 10}Black  {c |}{col 18}{res}{space 2} .8065503{col 30}{space 2} .6208001{col 41}{space 1}    1.30{col 50}{space 3}0.194{col 58}{space 4}-.4101956{col 71}{space 3} 2.023296
{txt}{space 7}Hispanic  {c |}{col 18}{res}{space 2}-.3245662{col 30}{space 2} .4515714{col 41}{space 1}   -0.72{col 50}{space 3}0.472{col 58}{space 4} -1.20963{col 71}{space 3} .5604974
{txt}{space 10}Other  {c |}{col 18}{res}{space 2}-.8135598{col 30}{space 2} 1.044737{col 41}{space 1}   -0.78{col 50}{space 3}0.436{col 58}{space 4}-2.861206{col 71}{space 3} 1.234087
{txt}{space 16} {c |}
{space 13}age {c |}{col 18}{res}{space 2} .0012933{col 30}{space 2} .0080827{col 41}{space 1}    0.16{col 50}{space 3}0.873{col 58}{space 4}-.0145485{col 71}{space 3} .0171352
{txt}{space 12}educ {c |}{col 18}{res}{space 2} .0489906{col 30}{space 2}  .124049{col 41}{space 1}    0.39{col 50}{space 3}0.693{col 58}{space 4}-.1941409{col 71}{space 3} .2921221
{txt}{space 10}income {c |}{col 18}{res}{space 2}-.0006243{col 30}{space 2} .0321023{col 41}{space 1}   -0.02{col 50}{space 3}0.984{col 58}{space 4}-.0635437{col 71}{space 3} .0622952
{txt}{space 16} {c |}
{space 9}marital {c |}
{space 8}Married  {c |}{col 18}{res}{space 2} .0780869{col 30}{space 2} .2940471{col 41}{space 1}    0.27{col 50}{space 3}0.791{col 58}{space 4}-.4982348{col 71}{space 3} .6544086
{txt}{space 7}evaluate1 {c |}{col 18}{res}{space 2} .1099944{col 30}{space 2} .3353757{col 41}{space 1}    0.33{col 50}{space 3}0.743{col 58}{space 4}-.5473298{col 71}{space 3} .7673187
{txt}{space 12}nfc1 {c |}{col 18}{res}{space 2}-.2371642{col 30}{space 2} .1704796{col 41}{space 1}   -1.39{col 50}{space 3}0.164{col 58}{space 4}-.5712982{col 71}{space 3} .0969697
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
           /cut1 {c |}{col 18}{res}{space 2}-1.207999{col 30}{space 2} .9464084{col 58}{space 4}-3.062925{col 71}{space 3} .6469272
{txt}           /cut2 {c |}{col 18}{res}{space 2} .5420484{col 30}{space 2} .8990287{col 58}{space 4}-1.220015{col 71}{space 3} 2.304112
{txt}           /cut3 {c |}{col 18}{res}{space 2}  2.13213{col 30}{space 2} .9178384{col 58}{space 4} .3331998{col 71}{space 3}  3.93106
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -870.1663}  
Iteration 1:{space 3}log pseudolikelihood = {res:-809.97842}  
Iteration 2:{space 3}log pseudolikelihood = {res:-809.12966}  
Iteration 3:{space 3}log pseudolikelihood = {res:-809.12773}  
Iteration 4:{space 3}log pseudolikelihood = {res:-809.12773}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       758
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     49.98
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-809.12773{txt}{col 51}Pseudo R2{col 67}= {res}    0.0701

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}    pid_str10_full{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      z{col 52}   P>|z|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
disagree_total_int {c |}{col 20}{res}{space 2}-.0732646{col 32}{space 2} .0159052{col 43}{space 1}   -4.61{col 52}{space 3}0.000{col 60}{space 4}-.1044383{col 73}{space 3}-.0420909
{txt}{space 9}numgiven1 {c |}{col 20}{res}{space 2} .0027667{col 32}{space 2} .1847226{col 43}{space 1}    0.01{col 52}{space 3}0.988{col 60}{space 4}-.3592831{col 73}{space 3} .3648164
{txt}{space 2}network_interest {c |}{col 20}{res}{space 2} .0870234{col 32}{space 2} .1129492{col 43}{space 1}    0.77{col 52}{space 3}0.441{col 60}{space 4} -.134353{col 73}{space 3} .3083998
{txt}{space 18} {c |}
{space 12}pid_21 {c |}
{space 9}Democrat  {c |}{col 20}{res}{space 2} .0252074{col 32}{space 2} .2106239{col 43}{space 1}    0.12{col 52}{space 3}0.905{col 60}{space 4}-.3876078{col 73}{space 3} .4380227
{txt}{space 7}interest_w1 {c |}{col 20}{res}{space 2} .0836825{col 32}{space 2} .1370415{col 43}{space 1}    0.61{col 52}{space 3}0.541{col 60}{space 4}-.1849139{col 73}{space 3}  .352279
{txt}{space 18} {c |}
{space 12}gender {c |}
{space 11}Female  {c |}{col 20}{res}{space 2}  .594655{col 32}{space 2} .2256873{col 43}{space 1}    2.63{col 52}{space 3}0.008{col 60}{space 4} .1523159{col 73}{space 3} 1.036994
{txt}{space 18} {c |}
{space 10}race_eth {c |}
{space 12}Black  {c |}{col 20}{res}{space 2} .7805026{col 32}{space 2} .6297975{col 43}{space 1}    1.24{col 52}{space 3}0.215{col 60}{space 4}-.4538779{col 73}{space 3} 2.014883
{txt}{space 9}Hispanic  {c |}{col 20}{res}{space 2}-.3629549{col 32}{space 2}   .46045{col 43}{space 1}   -0.79{col 52}{space 3}0.431{col 60}{space 4} -1.26542{col 73}{space 3} .5395106
{txt}{space 12}Other  {c |}{col 20}{res}{space 2}-.8662798{col 32}{space 2}  1.02271{col 43}{space 1}   -0.85{col 52}{space 3}0.397{col 60}{space 4}-2.870754{col 73}{space 3} 1.138194
{txt}{space 18} {c |}
{space 15}age {c |}{col 20}{res}{space 2} .0016055{col 32}{space 2} .0082833{col 43}{space 1}    0.19{col 52}{space 3}0.846{col 60}{space 4}-.0146294{col 73}{space 3} .0178403
{txt}{space 14}educ {c |}{col 20}{res}{space 2}  .049177{col 32}{space 2} .1246678{col 43}{space 1}    0.39{col 52}{space 3}0.693{col 60}{space 4}-.1951674{col 73}{space 3} .2935214
{txt}{space 12}income {c |}{col 20}{res}{space 2} .0033826{col 32}{space 2} .0328025{col 43}{space 1}    0.10{col 52}{space 3}0.918{col 60}{space 4}-.0609091{col 73}{space 3} .0676743
{txt}{space 18} {c |}
{space 11}marital {c |}
{space 10}Married  {c |}{col 20}{res}{space 2} .0526203{col 32}{space 2} .2940208{col 43}{space 1}    0.18{col 52}{space 3}0.858{col 60}{space 4}-.5236499{col 73}{space 3} .6288904
{txt}{space 9}evaluate1 {c |}{col 20}{res}{space 2} .1172453{col 32}{space 2} .3364816{col 43}{space 1}    0.35{col 52}{space 3}0.728{col 60}{space 4}-.5422466{col 73}{space 3} .7767372
{txt}{space 14}nfc1 {c |}{col 20}{res}{space 2}-.2321835{col 32}{space 2} .1724529{col 43}{space 1}   -1.35{col 52}{space 3}0.178{col 60}{space 4}-.5701849{col 73}{space 3}  .105818
{txt}{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
             /cut1 {c |}{col 20}{res}{space 2}-1.474836{col 32}{space 2} .9463421{col 60}{space 4}-3.329633{col 73}{space 3} .3799602
{txt}             /cut2 {c |}{col 20}{res}{space 2} .2606038{col 32}{space 2}  .884145{col 60}{space 4}-1.472289{col 73}{space 3} 1.993496
{txt}             /cut3 {c |}{col 20}{res}{space 2} 1.842653{col 32}{space 2}   .90251{col 60}{space 4}  .073766{col 73}{space 3}  3.61154
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -870.1663}  
Iteration 1:{space 3}log pseudolikelihood = {res:-805.13939}  
Iteration 2:{space 3}log pseudolikelihood = {res:-804.12178}  
Iteration 3:{space 3}log pseudolikelihood = {res:-804.11902}  
Iteration 4:{space 3}log pseudolikelihood = {res:-804.11902}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       758
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     51.88
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-804.11902{txt}{col 51}Pseudo R2{col 67}= {res}    0.0759

{txt}{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}      pid_str10_full{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
disagree_total_close {c |}{col 22}{res}{space 2}-.0762698{col 34}{space 2} .0160337{col 45}{space 1}   -4.76{col 54}{space 3}0.000{col 62}{space 4}-.1076953{col 75}{space 3}-.0448442
{txt}{space 11}numgiven1 {c |}{col 22}{res}{space 2} .0020779{col 34}{space 2} .1858231{col 45}{space 1}    0.01{col 54}{space 3}0.991{col 62}{space 4}-.3621287{col 75}{space 3} .3662844
{txt}{space 4}network_interest {c |}{col 22}{res}{space 2} .1432526{col 34}{space 2} .1129934{col 45}{space 1}    1.27{col 54}{space 3}0.205{col 62}{space 4}-.0782104{col 75}{space 3} .3647157
{txt}{space 20} {c |}
{space 14}pid_21 {c |}
{space 11}Democrat  {c |}{col 22}{res}{space 2} .0385035{col 34}{space 2} .2119812{col 45}{space 1}    0.18{col 54}{space 3}0.856{col 62}{space 4}-.3769719{col 75}{space 3}  .453979
{txt}{space 9}interest_w1 {c |}{col 22}{res}{space 2} .1164381{col 34}{space 2} .1349572{col 45}{space 1}    0.86{col 54}{space 3}0.388{col 62}{space 4}-.1480733{col 75}{space 3} .3809494
{txt}{space 20} {c |}
{space 14}gender {c |}
{space 13}Female  {c |}{col 22}{res}{space 2} .5626857{col 34}{space 2} .2235217{col 45}{space 1}    2.52{col 54}{space 3}0.012{col 62}{space 4} .1245911{col 75}{space 3}  1.00078
{txt}{space 20} {c |}
{space 12}race_eth {c |}
{space 14}Black  {c |}{col 22}{res}{space 2} .7831809{col 34}{space 2} .6314913{col 45}{space 1}    1.24{col 54}{space 3}0.215{col 62}{space 4}-.4545193{col 75}{space 3} 2.020881
{txt}{space 11}Hispanic  {c |}{col 22}{res}{space 2}-.2754111{col 34}{space 2} .4455598{col 45}{space 1}   -0.62{col 54}{space 3}0.536{col 62}{space 4}-1.148692{col 75}{space 3} .5978701
{txt}{space 14}Other  {c |}{col 22}{res}{space 2} -.906338{col 34}{space 2} 1.094383{col 45}{space 1}   -0.83{col 54}{space 3}0.408{col 62}{space 4} -3.05129{col 75}{space 3} 1.238614
{txt}{space 20} {c |}
{space 17}age {c |}{col 22}{res}{space 2} .0009369{col 34}{space 2} .0082842{col 45}{space 1}    0.11{col 54}{space 3}0.910{col 62}{space 4}-.0152998{col 75}{space 3} .0171736
{txt}{space 16}educ {c |}{col 22}{res}{space 2} .0432168{col 34}{space 2} .1242551{col 45}{space 1}    0.35{col 54}{space 3}0.728{col 62}{space 4}-.2003186{col 75}{space 3} .2867522
{txt}{space 14}income {c |}{col 22}{res}{space 2} .0031551{col 34}{space 2} .0323743{col 45}{space 1}    0.10{col 54}{space 3}0.922{col 62}{space 4}-.0602974{col 75}{space 3} .0666077
{txt}{space 20} {c |}
{space 13}marital {c |}
{space 12}Married  {c |}{col 22}{res}{space 2} .0395544{col 34}{space 2} .2955025{col 45}{space 1}    0.13{col 54}{space 3}0.894{col 62}{space 4}-.5396198{col 75}{space 3} .6187286
{txt}{space 11}evaluate1 {c |}{col 22}{res}{space 2} .0718735{col 34}{space 2} .3424879{col 45}{space 1}    0.21{col 54}{space 3}0.834{col 62}{space 4}-.5993905{col 75}{space 3} .7431375
{txt}{space 16}nfc1 {c |}{col 22}{res}{space 2}-.2276457{col 34}{space 2} .1706629{col 45}{space 1}   -1.33{col 54}{space 3}0.182{col 62}{space 4}-.5621388{col 75}{space 3} .1068475
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
               /cut1 {c |}{col 22}{res}{space 2}-1.248169{col 34}{space 2} .9234541{col 62}{space 4}-3.058105{col 75}{space 3} .5617683
{txt}               /cut2 {c |}{col 22}{res}{space 2}  .511386{col 34}{space 2} .8739639{col 62}{space 4}-1.201552{col 75}{space 3} 2.224324
{txt}               /cut3 {c |}{col 22}{res}{space 2} 2.109448{col 34}{space 2} .8991478{col 62}{space 4} .3471507{col 75}{space 3} 3.871745
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -870.1663}  
Iteration 1:{space 3}log pseudolikelihood = {res:-802.88845}  
Iteration 2:{space 3}log pseudolikelihood = {res:-801.90199}  
Iteration 3:{space 3}log pseudolikelihood = {res:-801.89943}  
Iteration 4:{space 3}log pseudolikelihood = {res:-801.89943}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       758
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     58.45
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-801.89943{txt}{col 51}Pseudo R2{col 67}= {res}    0.0785

{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}    Robust
{col 1}       pid_str10_full{col 23}{c |}      Coef.{col 35}   Std. Err.{col 47}      z{col 55}   P>|z|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
disagree_total_weight {c |}{col 23}{res}{space 2}-.0906986{col 35}{space 2} .0167124{col 46}{space 1}   -5.43{col 55}{space 3}0.000{col 63}{space 4}-.1234543{col 76}{space 3}-.0579428
{txt}{space 12}numgiven1 {c |}{col 23}{res}{space 2}-.0662509{col 35}{space 2} .1838159{col 46}{space 1}   -0.36{col 55}{space 3}0.719{col 63}{space 4}-.4265235{col 76}{space 3} .2940217
{txt}{space 5}network_interest {c |}{col 23}{res}{space 2} .1375239{col 35}{space 2} .1164046{col 46}{space 1}    1.18{col 55}{space 3}0.237{col 63}{space 4}-.0906249{col 76}{space 3} .3656727
{txt}{space 21} {c |}
{space 15}pid_21 {c |}
{space 12}Democrat  {c |}{col 23}{res}{space 2} .0671243{col 35}{space 2} .2123356{col 46}{space 1}    0.32{col 55}{space 3}0.752{col 63}{space 4}-.3490459{col 76}{space 3} .4832944
{txt}{space 10}interest_w1 {c |}{col 23}{res}{space 2}  .121205{col 35}{space 2} .1382107{col 46}{space 1}    0.88{col 55}{space 3}0.381{col 63}{space 4}-.1496831{col 76}{space 3} .3920931
{txt}{space 21} {c |}
{space 15}gender {c |}
{space 14}Female  {c |}{col 23}{res}{space 2} .5715818{col 35}{space 2} .2243604{col 46}{space 1}    2.55{col 55}{space 3}0.011{col 63}{space 4} .1318436{col 76}{space 3}  1.01132
{txt}{space 21} {c |}
{space 13}race_eth {c |}
{space 15}Black  {c |}{col 23}{res}{space 2} .7326711{col 35}{space 2} .6155813{col 46}{space 1}    1.19{col 55}{space 3}0.234{col 63}{space 4} -.473846{col 76}{space 3} 1.939188
{txt}{space 12}Hispanic  {c |}{col 23}{res}{space 2}-.2992347{col 35}{space 2} .4456883{col 46}{space 1}   -0.67{col 55}{space 3}0.502{col 63}{space 4}-1.172768{col 76}{space 3} .5742984
{txt}{space 15}Other  {c |}{col 23}{res}{space 2}-.8459283{col 35}{space 2} 1.070483{col 46}{space 1}   -0.79{col 55}{space 3}0.429{col 63}{space 4}-2.944037{col 76}{space 3} 1.252181
{txt}{space 21} {c |}
{space 18}age {c |}{col 23}{res}{space 2}-.0000792{col 35}{space 2}  .008307{col 46}{space 1}   -0.01{col 55}{space 3}0.992{col 63}{space 4}-.0163606{col 76}{space 3} .0162022
{txt}{space 17}educ {c |}{col 23}{res}{space 2} .0639091{col 35}{space 2} .1244864{col 46}{space 1}    0.51{col 55}{space 3}0.608{col 63}{space 4}-.1800796{col 76}{space 3} .3078979
{txt}{space 15}income {c |}{col 23}{res}{space 2} .0005612{col 35}{space 2} .0320802{col 46}{space 1}    0.02{col 55}{space 3}0.986{col 63}{space 4}-.0623149{col 76}{space 3} .0634373
{txt}{space 21} {c |}
{space 14}marital {c |}
{space 13}Married  {c |}{col 23}{res}{space 2} .0645069{col 35}{space 2} .2961168{col 46}{space 1}    0.22{col 55}{space 3}0.828{col 63}{space 4}-.5158712{col 76}{space 3} .6448851
{txt}{space 12}evaluate1 {c |}{col 23}{res}{space 2} .0698314{col 35}{space 2} .3393192{col 46}{space 1}    0.21{col 55}{space 3}0.837{col 63}{space 4} -.595222{col 76}{space 3} .7348848
{txt}{space 17}nfc1 {c |}{col 23}{res}{space 2}-.2272071{col 35}{space 2} .1738196{col 46}{space 1}   -1.31{col 55}{space 3}0.191{col 63}{space 4}-.5678872{col 76}{space 3}  .113473
{txt}{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                /cut1 {c |}{col 23}{res}{space 2}-1.297041{col 35}{space 2}  .926273{col 63}{space 4}-3.112502{col 76}{space 3} .5184211
{txt}                /cut2 {c |}{col 23}{res}{space 2} .4564006{col 35}{space 2} .8698205{col 63}{space 4}-1.248416{col 76}{space 3} 2.161218
{txt}                /cut3 {c |}{col 23}{res}{space 2} 2.060888{col 35}{space 2} .8890291{col 63}{space 4} .3184228{col 76}{space 3} 3.803353
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est5{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-870.83617}  
Iteration 1:{space 3}log pseudolikelihood = {res:-818.13086}  
Iteration 2:{space 3}log pseudolikelihood = {res:-817.57639}  
Iteration 3:{space 3}log pseudolikelihood = {res:-817.57576}  
Iteration 4:{space 3}log pseudolikelihood = {res:-817.57576}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       759
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     62.59
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-817.57576{txt}{col 51}Pseudo R2{col 67}= {res}    0.0612

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}  pid_str10_full{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}gendiff {c |}{col 18}{res}{space 2} -.535549{col 30}{space 2} .1291238{col 41}{space 1}   -4.15{col 50}{space 3}0.000{col 58}{space 4}-.7886269{col 71}{space 3} -.282471
{txt}{space 7}numgiven1 {c |}{col 18}{res}{space 2} .0993916{col 30}{space 2} .1773467{col 41}{space 1}    0.56{col 50}{space 3}0.575{col 58}{space 4}-.2482016{col 71}{space 3} .4469848
{txt}network_interest {c |}{col 18}{res}{space 2} .1241221{col 30}{space 2} .1226479{col 41}{space 1}    1.01{col 50}{space 3}0.312{col 58}{space 4}-.1162634{col 71}{space 3} .3645077
{txt}{space 16} {c |}
{space 10}pid_21 {c |}
{space 7}Democrat  {c |}{col 18}{res}{space 2} .1115482{col 30}{space 2}  .214794{col 41}{space 1}    0.52{col 50}{space 3}0.604{col 58}{space 4}-.3094403{col 71}{space 3} .5325367
{txt}{space 5}interest_w1 {c |}{col 18}{res}{space 2} .1608683{col 30}{space 2} .1355668{col 41}{space 1}    1.19{col 50}{space 3}0.235{col 58}{space 4}-.1048378{col 71}{space 3} .4265744
{txt}{space 16} {c |}
{space 10}gender {c |}
{space 9}Female  {c |}{col 18}{res}{space 2} .6582146{col 30}{space 2} .2213548{col 41}{space 1}    2.97{col 50}{space 3}0.003{col 58}{space 4} .2243672{col 71}{space 3} 1.092062
{txt}{space 16} {c |}
{space 8}race_eth {c |}
{space 10}Black  {c |}{col 18}{res}{space 2} .7388831{col 30}{space 2} .5282556{col 41}{space 1}    1.40{col 50}{space 3}0.162{col 58}{space 4}-.2964789{col 71}{space 3} 1.774245
{txt}{space 7}Hispanic  {c |}{col 18}{res}{space 2}-.3864572{col 30}{space 2} .4869861{col 41}{space 1}   -0.79{col 50}{space 3}0.427{col 58}{space 4}-1.340932{col 71}{space 3}  .568018
{txt}{space 10}Other  {c |}{col 18}{res}{space 2}-.8944338{col 30}{space 2} .7635842{col 41}{space 1}   -1.17{col 50}{space 3}0.241{col 58}{space 4}-2.391031{col 71}{space 3} .6021636
{txt}{space 16} {c |}
{space 13}age {c |}{col 18}{res}{space 2}-.0035222{col 30}{space 2}  .008063{col 41}{space 1}   -0.44{col 50}{space 3}0.662{col 58}{space 4}-.0193254{col 71}{space 3}  .012281
{txt}{space 12}educ {c |}{col 18}{res}{space 2}   .10706{col 30}{space 2} .1214046{col 41}{space 1}    0.88{col 50}{space 3}0.378{col 58}{space 4}-.1308886{col 71}{space 3} .3450086
{txt}{space 10}income {c |}{col 18}{res}{space 2}-.0070451{col 30}{space 2} .0299925{col 41}{space 1}   -0.23{col 50}{space 3}0.814{col 58}{space 4}-.0658292{col 71}{space 3}  .051739
{txt}{space 16} {c |}
{space 9}marital {c |}
{space 8}Married  {c |}{col 18}{res}{space 2} .0863082{col 30}{space 2} .2774773{col 41}{space 1}    0.31{col 50}{space 3}0.756{col 58}{space 4}-.4575373{col 71}{space 3} .6301536
{txt}{space 7}evaluate1 {c |}{col 18}{res}{space 2}  .178207{col 30}{space 2} .3204711{col 41}{space 1}    0.56{col 50}{space 3}0.578{col 58}{space 4}-.4499047{col 71}{space 3} .8063187
{txt}{space 12}nfc1 {c |}{col 18}{res}{space 2}-.2376286{col 30}{space 2} .1725555{col 41}{space 1}   -1.38{col 50}{space 3}0.168{col 58}{space 4}-.5758312{col 71}{space 3}  .100574
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
           /cut1 {c |}{col 18}{res}{space 2} -2.23346{col 30}{space 2} .9654421{col 58}{space 4}-4.125692{col 71}{space 3}-.3412287
{txt}           /cut2 {c |}{col 18}{res}{space 2}-.5461155{col 30}{space 2} .8968235{col 58}{space 4}-2.303857{col 71}{space 3} 1.211626
{txt}           /cut3 {c |}{col 18}{res}{space 2} .9996475{col 30}{space 2}  .899483{col 58}{space 4}-.7633068{col 71}{space 3} 2.762602
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est6{txt} stored)

{com}.         
. esttab using 2008OCT_ALTMEASURES_PARTISANSHIP.rtf, onecell nobaselevels replace label pr2 aic bic se star(+ 0.10 * 0.05 ** 0.01) ///
>         mtitles("Original Measure" "(/D+A)" "Weigh: by Disc. Interest" ///
>                 "Weigh: by Tie Strength" "Weighted by Gen Dis" "Gen Disagree")  ///
>         title({c -(}\b Table XX.{c )-} "Alternative Measures of Disagreement - 2008-2009 ANES (October)") ///
>         rename(disagree_total Disagreement disagree_avg "Disagreement" disagree_total_int Disagreement disagree_total_close Disagreement ///
>                                 disagree_total_weight Disagreement gendiff Disagreement) 
{res}{txt}(output written to {browse  `"2008OCT_ALTMEASURES_PARTISANSHIP.rtf"'})

{com}. 
. eststo clear
{txt}
{com}.         
. *W11*
. eststo clear
{txt}
{com}. foreach var in disagree_total disagree_avg disagree_total_int disagree_total_close  ///
>         disagree_total_weight gendiff  {c -(}
{txt}  2{com}.         eststo: ologit pid_str11_full `var' numgiven1 network_interest i.pid_21 interest_w1 i.gender ///
>                         i.race age educ income i.marital evaluate1 nfc1 [pweight= WGTC11]
{txt}  3{com}.                 {c )-}       

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-842.05531}  
Iteration 1:{space 3}log pseudolikelihood = {res:-772.53235}  
Iteration 2:{space 3}log pseudolikelihood = {res:-771.47864}  
Iteration 3:{space 3}log pseudolikelihood = {res:-771.47703}  
Iteration 4:{space 3}log pseudolikelihood = {res:-771.47703}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       758
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     93.27
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-771.47703{txt}{col 51}Pseudo R2{col 67}= {res}    0.0838

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}  pid_str11_full{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}disagree_total {c |}{col 18}{res}{space 2}-.3096947{col 30}{space 2} .0587128{col 41}{space 1}   -5.27{col 50}{space 3}0.000{col 58}{space 4}-.4247697{col 71}{space 3}-.1946196
{txt}{space 7}numgiven1 {c |}{col 18}{res}{space 2}-.0473052{col 30}{space 2} .2433034{col 41}{space 1}   -0.19{col 50}{space 3}0.846{col 58}{space 4}-.5241712{col 71}{space 3} .4295608
{txt}network_interest {c |}{col 18}{res}{space 2} .1717652{col 30}{space 2} .1161907{col 41}{space 1}    1.48{col 50}{space 3}0.139{col 58}{space 4}-.0559644{col 71}{space 3} .3994948
{txt}{space 16} {c |}
{space 10}pid_21 {c |}
{space 7}Democrat  {c |}{col 18}{res}{space 2} .0978764{col 30}{space 2} .2205888{col 41}{space 1}    0.44{col 50}{space 3}0.657{col 58}{space 4}-.3344697{col 71}{space 3} .5302224
{txt}{space 5}interest_w1 {c |}{col 18}{res}{space 2} .1776006{col 30}{space 2} .1380135{col 41}{space 1}    1.29{col 50}{space 3}0.198{col 58}{space 4} -.092901{col 71}{space 3} .4481021
{txt}{space 16} {c |}
{space 10}gender {c |}
{space 9}Female  {c |}{col 18}{res}{space 2} .5787385{col 30}{space 2} .2255295{col 41}{space 1}    2.57{col 50}{space 3}0.010{col 58}{space 4} .1367087{col 71}{space 3} 1.020768
{txt}{space 16} {c |}
{space 8}race_eth {c |}
{space 10}Black  {c |}{col 18}{res}{space 2} .1692466{col 30}{space 2} .6200104{col 41}{space 1}    0.27{col 50}{space 3}0.785{col 58}{space 4}-1.045951{col 71}{space 3} 1.384445
{txt}{space 7}Hispanic  {c |}{col 18}{res}{space 2}-.8211729{col 30}{space 2} .4208676{col 41}{space 1}   -1.95{col 50}{space 3}0.051{col 58}{space 4}-1.646058{col 71}{space 3} .0037125
{txt}{space 10}Other  {c |}{col 18}{res}{space 2}-.0895725{col 30}{space 2} 1.050263{col 41}{space 1}   -0.09{col 50}{space 3}0.932{col 58}{space 4}-2.148051{col 71}{space 3} 1.968906
{txt}{space 16} {c |}
{space 13}age {c |}{col 18}{res}{space 2}-.0040234{col 30}{space 2} .0075791{col 41}{space 1}   -0.53{col 50}{space 3}0.596{col 58}{space 4}-.0188782{col 71}{space 3} .0108314
{txt}{space 12}educ {c |}{col 18}{res}{space 2} .0733878{col 30}{space 2} .1247128{col 41}{space 1}    0.59{col 50}{space 3}0.556{col 58}{space 4}-.1710449{col 71}{space 3} .3178205
{txt}{space 10}income {c |}{col 18}{res}{space 2}-.0280734{col 30}{space 2} .0296901{col 41}{space 1}   -0.95{col 50}{space 3}0.344{col 58}{space 4}-.0862649{col 71}{space 3} .0301182
{txt}{space 16} {c |}
{space 9}marital {c |}
{space 8}Married  {c |}{col 18}{res}{space 2} .0827636{col 30}{space 2} .2802861{col 41}{space 1}    0.30{col 50}{space 3}0.768{col 58}{space 4}-.4665871{col 71}{space 3} .6321143
{txt}{space 7}evaluate1 {c |}{col 18}{res}{space 2} .3304835{col 30}{space 2} .3170523{col 41}{space 1}    1.04{col 50}{space 3}0.297{col 58}{space 4}-.2909276{col 71}{space 3} .9518946
{txt}{space 12}nfc1 {c |}{col 18}{res}{space 2}-.3415686{col 30}{space 2} .1718687{col 41}{space 1}   -1.99{col 50}{space 3}0.047{col 58}{space 4} -.678425{col 71}{space 3}-.0047121
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
           /cut1 {c |}{col 18}{res}{space 2}-1.496301{col 30}{space 2} 1.030774{col 58}{space 4}-3.516582{col 71}{space 3} .5239795
{txt}           /cut2 {c |}{col 18}{res}{space 2} .1435725{col 30}{space 2} .9611172{col 58}{space 4}-1.740183{col 71}{space 3} 2.027328
{txt}           /cut3 {c |}{col 18}{res}{space 2} 1.502076{col 30}{space 2} .9621703{col 58}{space 4}-.3837429{col 71}{space 3} 3.387895
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-842.05531}  
Iteration 1:{space 3}log pseudolikelihood = {res:-772.62647}  
Iteration 2:{space 3}log pseudolikelihood = {res:-771.47569}  
Iteration 3:{space 3}log pseudolikelihood = {res:-771.47388}  
Iteration 4:{space 3}log pseudolikelihood = {res:-771.47388}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       758
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     84.27
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-771.47388{txt}{col 51}Pseudo R2{col 67}= {res}    0.0838

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}  pid_str11_full{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}disagree_avg {c |}{col 18}{res}{space 2}-.8501335{col 30}{space 2} .1710605{col 41}{space 1}   -4.97{col 50}{space 3}0.000{col 58}{space 4}-1.185406{col 71}{space 3}-.5148611
{txt}{space 7}numgiven1 {c |}{col 18}{res}{space 2}-.0281927{col 30}{space 2} .2545678{col 41}{space 1}   -0.11{col 50}{space 3}0.912{col 58}{space 4}-.5271364{col 71}{space 3}  .470751
{txt}network_interest {c |}{col 18}{res}{space 2} .1647063{col 30}{space 2} .1168477{col 41}{space 1}    1.41{col 50}{space 3}0.159{col 58}{space 4}-.0643111{col 71}{space 3} .3937236
{txt}{space 16} {c |}
{space 10}pid_21 {c |}
{space 7}Democrat  {c |}{col 18}{res}{space 2} .0857338{col 30}{space 2} .2184831{col 41}{space 1}    0.39{col 50}{space 3}0.695{col 58}{space 4}-.3424852{col 71}{space 3} .5139528
{txt}{space 5}interest_w1 {c |}{col 18}{res}{space 2} .1779353{col 30}{space 2} .1359139{col 41}{space 1}    1.31{col 50}{space 3}0.190{col 58}{space 4}-.0884511{col 71}{space 3} .4443216
{txt}{space 16} {c |}
{space 10}gender {c |}
{space 9}Female  {c |}{col 18}{res}{space 2} .5803148{col 30}{space 2} .2219642{col 41}{space 1}    2.61{col 50}{space 3}0.009{col 58}{space 4} .1452729{col 71}{space 3} 1.015357
{txt}{space 16} {c |}
{space 8}race_eth {c |}
{space 10}Black  {c |}{col 18}{res}{space 2} .1874092{col 30}{space 2} .6151058{col 41}{space 1}    0.30{col 50}{space 3}0.761{col 58}{space 4}-1.018176{col 71}{space 3} 1.392994
{txt}{space 7}Hispanic  {c |}{col 18}{res}{space 2}-.8197589{col 30}{space 2} .3912013{col 41}{space 1}   -2.10{col 50}{space 3}0.036{col 58}{space 4}-1.586499{col 71}{space 3}-.0530184
{txt}{space 10}Other  {c |}{col 18}{res}{space 2}-.0624368{col 30}{space 2} 1.004506{col 41}{space 1}   -0.06{col 50}{space 3}0.950{col 58}{space 4}-2.031233{col 71}{space 3}  1.90636
{txt}{space 16} {c |}
{space 13}age {c |}{col 18}{res}{space 2}-.0034134{col 30}{space 2} .0074137{col 41}{space 1}   -0.46{col 50}{space 3}0.645{col 58}{space 4}-.0179439{col 71}{space 3} .0111171
{txt}{space 12}educ {c |}{col 18}{res}{space 2} .0726624{col 30}{space 2} .1230977{col 41}{space 1}    0.59{col 50}{space 3}0.555{col 58}{space 4}-.1686046{col 71}{space 3} .3139294
{txt}{space 10}income {c |}{col 18}{res}{space 2}-.0301293{col 30}{space 2} .0291386{col 41}{space 1}   -1.03{col 50}{space 3}0.301{col 58}{space 4}-.0872399{col 71}{space 3} .0269813
{txt}{space 16} {c |}
{space 9}marital {c |}
{space 8}Married  {c |}{col 18}{res}{space 2} .1200737{col 30}{space 2} .2770918{col 41}{space 1}    0.43{col 50}{space 3}0.665{col 58}{space 4}-.4230162{col 71}{space 3} .6631635
{txt}{space 7}evaluate1 {c |}{col 18}{res}{space 2} .3351262{col 30}{space 2} .3128576{col 41}{space 1}    1.07{col 50}{space 3}0.284{col 58}{space 4}-.2780635{col 71}{space 3} .9483159
{txt}{space 12}nfc1 {c |}{col 18}{res}{space 2}-.3424228{col 30}{space 2} .1696348{col 41}{space 1}   -2.02{col 50}{space 3}0.044{col 58}{space 4}-.6749009{col 71}{space 3}-.0099447
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
           /cut1 {c |}{col 18}{res}{space 2}-1.453869{col 30}{space 2} .9891667{col 58}{space 4}  -3.3926{col 71}{space 3} .4848625
{txt}           /cut2 {c |}{col 18}{res}{space 2} .1909153{col 30}{space 2} .9238198{col 58}{space 4}-1.619738{col 71}{space 3} 2.001569
{txt}           /cut3 {c |}{col 18}{res}{space 2} 1.549175{col 30}{space 2} .9219844{col 58}{space 4}-.2578809{col 71}{space 3} 3.356231
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-842.05531}  
Iteration 1:{space 3}log pseudolikelihood = {res:-771.85901}  
Iteration 2:{space 3}log pseudolikelihood = {res:-770.80609}  
Iteration 3:{space 3}log pseudolikelihood = {res:-770.80434}  
Iteration 4:{space 3}log pseudolikelihood = {res:-770.80434}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       758
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     89.97
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-770.80434{txt}{col 51}Pseudo R2{col 67}= {res}    0.0846

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}    pid_str11_full{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      z{col 52}   P>|z|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
disagree_total_int {c |}{col 20}{res}{space 2}-.0830863{col 32}{space 2} .0157915{col 43}{space 1}   -5.26{col 52}{space 3}0.000{col 60}{space 4}-.1140371{col 73}{space 3}-.0521354
{txt}{space 9}numgiven1 {c |}{col 20}{res}{space 2}-.0537309{col 32}{space 2} .2443738{col 43}{space 1}   -0.22{col 52}{space 3}0.826{col 60}{space 4}-.5326948{col 73}{space 3} .4252329
{txt}{space 2}network_interest {c |}{col 20}{res}{space 2} .1081179{col 32}{space 2} .1183006{col 43}{space 1}    0.91{col 52}{space 3}0.361{col 60}{space 4}-.1237471{col 73}{space 3} .3399829
{txt}{space 18} {c |}
{space 12}pid_21 {c |}
{space 9}Democrat  {c |}{col 20}{res}{space 2} .0764917{col 32}{space 2} .2199194{col 43}{space 1}    0.35{col 52}{space 3}0.728{col 60}{space 4}-.3545423{col 73}{space 3} .5075257
{txt}{space 7}interest_w1 {c |}{col 20}{res}{space 2} .1615621{col 32}{space 2}  .140763{col 43}{space 1}    1.15{col 52}{space 3}0.251{col 60}{space 4}-.1143282{col 73}{space 3} .4374525
{txt}{space 18} {c |}
{space 12}gender {c |}
{space 11}Female  {c |}{col 20}{res}{space 2} .5942422{col 32}{space 2} .2240371{col 43}{space 1}    2.65{col 52}{space 3}0.008{col 60}{space 4} .1551376{col 73}{space 3} 1.033347
{txt}{space 18} {c |}
{space 10}race_eth {c |}
{space 12}Black  {c |}{col 20}{res}{space 2} .1682656{col 32}{space 2} .6300215{col 43}{space 1}    0.27{col 52}{space 3}0.789{col 60}{space 4}-1.066554{col 73}{space 3} 1.403085
{txt}{space 9}Hispanic  {c |}{col 20}{res}{space 2}-.8346483{col 32}{space 2} .4205005{col 43}{space 1}   -1.98{col 52}{space 3}0.047{col 60}{space 4}-1.658814{col 73}{space 3}-.0104824
{txt}{space 12}Other  {c |}{col 20}{res}{space 2}-.1188771{col 32}{space 2} 1.014349{col 43}{space 1}   -0.12{col 52}{space 3}0.907{col 60}{space 4}-2.106964{col 73}{space 3}  1.86921
{txt}{space 18} {c |}
{space 15}age {c |}{col 20}{res}{space 2}-.0026669{col 32}{space 2} .0076379{col 43}{space 1}   -0.35{col 52}{space 3}0.727{col 60}{space 4}-.0176369{col 73}{space 3} .0123031
{txt}{space 14}educ {c |}{col 20}{res}{space 2} .0649446{col 32}{space 2} .1290136{col 43}{space 1}    0.50{col 52}{space 3}0.615{col 60}{space 4}-.1879175{col 73}{space 3} .3178067
{txt}{space 12}income {c |}{col 20}{res}{space 2}-.0222498{col 32}{space 2} .0306169{col 43}{space 1}   -0.73{col 52}{space 3}0.467{col 60}{space 4}-.0822578{col 73}{space 3} .0377582
{txt}{space 18} {c |}
{space 11}marital {c |}
{space 10}Married  {c |}{col 20}{res}{space 2} .0869573{col 32}{space 2} .2809313{col 43}{space 1}    0.31{col 52}{space 3}0.757{col 60}{space 4}-.4636579{col 73}{space 3} .6375726
{txt}{space 9}evaluate1 {c |}{col 20}{res}{space 2} .3413412{col 32}{space 2} .3179532{col 43}{space 1}    1.07{col 52}{space 3}0.283{col 60}{space 4}-.2818357{col 73}{space 3} .9645181
{txt}{space 14}nfc1 {c |}{col 20}{res}{space 2}-.3298874{col 32}{space 2} .1748106{col 43}{space 1}   -1.89{col 52}{space 3}0.059{col 60}{space 4}-.6725098{col 73}{space 3} .0127351
{txt}{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
             /cut1 {c |}{col 20}{res}{space 2} -1.64965{col 32}{space 2} 1.042661{col 60}{space 4}-3.693228{col 73}{space 3} .3939276
{txt}             /cut2 {c |}{col 20}{res}{space 2} -.014193{col 32}{space 2} .9725138{col 60}{space 4}-1.920285{col 73}{space 3} 1.891899
{txt}             /cut3 {c |}{col 20}{res}{space 2} 1.344922{col 32}{space 2} .9722972{col 60}{space 4}-.5607451{col 73}{space 3}  3.25059
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-842.05531}  
Iteration 1:{space 3}log pseudolikelihood = {res:-769.75702}  
Iteration 2:{space 3}log pseudolikelihood = {res:-768.62866}  
Iteration 3:{space 3}log pseudolikelihood = {res:-768.62674}  
Iteration 4:{space 3}log pseudolikelihood = {res:-768.62674}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       758
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     91.16
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-768.62674{txt}{col 51}Pseudo R2{col 67}= {res}    0.0872

{txt}{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}      pid_str11_full{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
disagree_total_close {c |}{col 22}{res}{space 2}-.0808041{col 34}{space 2} .0148286{col 45}{space 1}   -5.45{col 54}{space 3}0.000{col 62}{space 4}-.1098676{col 75}{space 3}-.0517406
{txt}{space 11}numgiven1 {c |}{col 22}{res}{space 2}-.0463531{col 34}{space 2} .2442176{col 45}{space 1}   -0.19{col 54}{space 3}0.849{col 62}{space 4}-.5250108{col 75}{space 3} .4323046
{txt}{space 4}network_interest {c |}{col 22}{res}{space 2} .1600245{col 34}{space 2} .1177043{col 45}{space 1}    1.36{col 54}{space 3}0.174{col 62}{space 4}-.0706718{col 75}{space 3} .3907207
{txt}{space 20} {c |}
{space 14}pid_21 {c |}
{space 11}Democrat  {c |}{col 22}{res}{space 2} .1010613{col 34}{space 2} .2224053{col 45}{space 1}    0.45{col 54}{space 3}0.650{col 62}{space 4} -.334845{col 75}{space 3} .5369676
{txt}{space 9}interest_w1 {c |}{col 22}{res}{space 2} .1950351{col 34}{space 2} .1364483{col 45}{space 1}    1.43{col 54}{space 3}0.153{col 62}{space 4}-.0723986{col 75}{space 3} .4624688
{txt}{space 20} {c |}
{space 14}gender {c |}
{space 13}Female  {c |}{col 22}{res}{space 2} .5548005{col 34}{space 2} .2242102{col 45}{space 1}    2.47{col 54}{space 3}0.013{col 62}{space 4} .1153567{col 75}{space 3} .9942444
{txt}{space 20} {c |}
{space 12}race_eth {c |}
{space 14}Black  {c |}{col 22}{res}{space 2} .1693658{col 34}{space 2} .6148444{col 45}{space 1}    0.28{col 54}{space 3}0.783{col 62}{space 4}-1.035707{col 75}{space 3} 1.374439
{txt}{space 11}Hispanic  {c |}{col 22}{res}{space 2}-.7447585{col 34}{space 2} .4269734{col 45}{space 1}   -1.74{col 54}{space 3}0.081{col 62}{space 4}-1.581611{col 75}{space 3}  .092094
{txt}{space 14}Other  {c |}{col 22}{res}{space 2}-.1383982{col 34}{space 2} 1.102243{col 45}{space 1}   -0.13{col 54}{space 3}0.900{col 62}{space 4}-2.298754{col 75}{space 3} 2.021958
{txt}{space 20} {c |}
{space 17}age {c |}{col 22}{res}{space 2}-.0040269{col 34}{space 2} .0075495{col 45}{space 1}   -0.53{col 54}{space 3}0.594{col 62}{space 4}-.0188236{col 75}{space 3} .0107698
{txt}{space 16}educ {c |}{col 22}{res}{space 2} .0581016{col 34}{space 2} .1243641{col 45}{space 1}    0.47{col 54}{space 3}0.640{col 62}{space 4}-.1856475{col 75}{space 3} .3018507
{txt}{space 14}income {c |}{col 22}{res}{space 2}-.0256532{col 34}{space 2} .0295373{col 45}{space 1}   -0.87{col 54}{space 3}0.385{col 62}{space 4}-.0835452{col 75}{space 3} .0322389
{txt}{space 20} {c |}
{space 13}marital {c |}
{space 12}Married  {c |}{col 22}{res}{space 2} .0804633{col 34}{space 2} .2804241{col 45}{space 1}    0.29{col 54}{space 3}0.774{col 62}{space 4}-.4691578{col 75}{space 3} .6300845
{txt}{space 11}evaluate1 {c |}{col 22}{res}{space 2} .3014705{col 34}{space 2} .3206169{col 45}{space 1}    0.94{col 54}{space 3}0.347{col 62}{space 4}-.3269271{col 75}{space 3} .9298681
{txt}{space 16}nfc1 {c |}{col 22}{res}{space 2}-.3288618{col 34}{space 2} .1715448{col 45}{space 1}   -1.92{col 54}{space 3}0.055{col 62}{space 4}-.6650834{col 75}{space 3} .0073598
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
               /cut1 {c |}{col 22}{res}{space 2}-1.489783{col 34}{space 2} 1.030565{col 62}{space 4}-3.509653{col 75}{space 3} .5300872
{txt}               /cut2 {c |}{col 22}{res}{space 2}   .15287{col 34}{space 2} .9568223{col 62}{space 4}-1.722467{col 75}{space 3} 2.028207
{txt}               /cut3 {c |}{col 22}{res}{space 2} 1.521737{col 34}{space 2} .9563828{col 62}{space 4}-.3527393{col 75}{space 3} 3.396213
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-842.05531}  
Iteration 1:{space 3}log pseudolikelihood = {res:-765.07697}  
Iteration 2:{space 3}log pseudolikelihood = {res:-763.84753}  
Iteration 3:{space 3}log pseudolikelihood = {res:-763.84507}  
Iteration 4:{space 3}log pseudolikelihood = {res:-763.84507}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       758
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}    117.49
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-763.84507{txt}{col 51}Pseudo R2{col 67}= {res}    0.0929

{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}    Robust
{col 1}       pid_str11_full{col 23}{c |}      Coef.{col 35}   Std. Err.{col 47}      z{col 55}   P>|z|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
disagree_total_weight {c |}{col 23}{res}{space 2}-.0987999{col 35}{space 2}  .015064{col 46}{space 1}   -6.56{col 55}{space 3}0.000{col 63}{space 4}-.1283249{col 76}{space 3}-.0692749
{txt}{space 12}numgiven1 {c |}{col 23}{res}{space 2}-.1201614{col 35}{space 2} .2482094{col 46}{space 1}   -0.48{col 55}{space 3}0.628{col 63}{space 4} -.606643{col 76}{space 3} .3663201
{txt}{space 5}network_interest {c |}{col 23}{res}{space 2} .1528833{col 35}{space 2} .1184723{col 46}{space 1}    1.29{col 55}{space 3}0.197{col 63}{space 4}-.0793182{col 76}{space 3} .3850847
{txt}{space 21} {c |}
{space 15}pid_21 {c |}
{space 12}Democrat  {c |}{col 23}{res}{space 2} .1172295{col 35}{space 2} .2224754{col 46}{space 1}    0.53{col 55}{space 3}0.598{col 63}{space 4}-.3188144{col 76}{space 3} .5532733
{txt}{space 10}interest_w1 {c |}{col 23}{res}{space 2} .2004928{col 35}{space 2} .1399235{col 46}{space 1}    1.43{col 55}{space 3}0.152{col 63}{space 4}-.0737523{col 76}{space 3} .4747379
{txt}{space 21} {c |}
{space 15}gender {c |}
{space 14}Female  {c |}{col 23}{res}{space 2} .5651977{col 35}{space 2} .2247214{col 46}{space 1}    2.52{col 55}{space 3}0.012{col 63}{space 4} .1247519{col 76}{space 3} 1.005643
{txt}{space 21} {c |}
{space 13}race_eth {c |}
{space 15}Black  {c |}{col 23}{res}{space 2}  .130389{col 35}{space 2} .6361366{col 46}{space 1}    0.20{col 55}{space 3}0.838{col 63}{space 4}-1.116416{col 76}{space 3} 1.377194
{txt}{space 12}Hispanic  {c |}{col 23}{res}{space 2}-.7462106{col 35}{space 2} .4203897{col 46}{space 1}   -1.78{col 55}{space 3}0.076{col 63}{space 4}-1.570159{col 76}{space 3} .0777381
{txt}{space 15}Other  {c |}{col 23}{res}{space 2}-.1044916{col 35}{space 2} .9715379{col 46}{space 1}   -0.11{col 55}{space 3}0.914{col 63}{space 4}-2.008671{col 76}{space 3} 1.799688
{txt}{space 21} {c |}
{space 18}age {c |}{col 23}{res}{space 2}-.0047925{col 35}{space 2} .0075716{col 46}{space 1}   -0.63{col 55}{space 3}0.527{col 63}{space 4}-.0196326{col 76}{space 3} .0100477
{txt}{space 17}educ {c |}{col 23}{res}{space 2} .0825711{col 35}{space 2} .1250462{col 46}{space 1}    0.66{col 55}{space 3}0.509{col 63}{space 4}-.1625149{col 76}{space 3} .3276572
{txt}{space 15}income {c |}{col 23}{res}{space 2}-.0283863{col 35}{space 2} .0288503{col 46}{space 1}   -0.98{col 55}{space 3}0.325{col 63}{space 4}-.0849319{col 76}{space 3} .0281593
{txt}{space 21} {c |}
{space 14}marital {c |}
{space 13}Married  {c |}{col 23}{res}{space 2} .1019033{col 35}{space 2} .2789856{col 46}{space 1}    0.37{col 55}{space 3}0.715{col 63}{space 4}-.4448983{col 76}{space 3}  .648705
{txt}{space 12}evaluate1 {c |}{col 23}{res}{space 2} .3087583{col 35}{space 2} .3157409{col 46}{space 1}    0.98{col 55}{space 3}0.328{col 63}{space 4}-.3100826{col 76}{space 3} .9275991
{txt}{space 17}nfc1 {c |}{col 23}{res}{space 2}-.3307281{col 35}{space 2} .1740539{col 46}{space 1}   -1.90{col 55}{space 3}0.057{col 63}{space 4}-.6718674{col 76}{space 3} .0104112
{txt}{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                /cut1 {c |}{col 23}{res}{space 2}-1.522578{col 35}{space 2}  1.04732{col 63}{space 4}-3.575288{col 76}{space 3} .5301311
{txt}                /cut2 {c |}{col 23}{res}{space 2} .1227636{col 35}{space 2} .9726463{col 63}{space 4}-1.783588{col 76}{space 3} 2.029115
{txt}                /cut3 {c |}{col 23}{res}{space 2} 1.504839{col 35}{space 2} .9699713{col 63}{space 4}  -.39627{col 76}{space 3} 3.405947
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est5{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-842.74163}  
Iteration 1:{space 3}log pseudolikelihood = {res:-785.02086}  
Iteration 2:{space 3}log pseudolikelihood = {res:  -784.347}  
Iteration 3:{space 3}log pseudolikelihood = {res:-784.34608}  
Iteration 4:{space 3}log pseudolikelihood = {res:-784.34608}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       759
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     69.89
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-784.34608{txt}{col 51}Pseudo R2{col 67}= {res}    0.0693

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}  pid_str11_full{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}gendiff {c |}{col 18}{res}{space 2} -.575245{col 30}{space 2} .1642725{col 41}{space 1}   -3.50{col 50}{space 3}0.000{col 58}{space 4}-.8972133{col 71}{space 3}-.2532768
{txt}{space 7}numgiven1 {c |}{col 18}{res}{space 2} .0377353{col 30}{space 2} .2560307{col 41}{space 1}    0.15{col 50}{space 3}0.883{col 58}{space 4}-.4640756{col 71}{space 3} .5395463
{txt}network_interest {c |}{col 18}{res}{space 2} .1533217{col 30}{space 2} .1163195{col 41}{space 1}    1.32{col 50}{space 3}0.187{col 58}{space 4}-.0746604{col 71}{space 3} .3813038
{txt}{space 16} {c |}
{space 10}pid_21 {c |}
{space 7}Democrat  {c |}{col 18}{res}{space 2} .0907589{col 30}{space 2} .2161471{col 41}{space 1}    0.42{col 50}{space 3}0.675{col 58}{space 4}-.3328816{col 71}{space 3} .5143995
{txt}{space 5}interest_w1 {c |}{col 18}{res}{space 2} .2308036{col 30}{space 2} .1325804{col 41}{space 1}    1.74{col 50}{space 3}0.082{col 58}{space 4}-.0290492{col 71}{space 3} .4906564
{txt}{space 16} {c |}
{space 10}gender {c |}
{space 9}Female  {c |}{col 18}{res}{space 2} .6664663{col 30}{space 2} .2191135{col 41}{space 1}    3.04{col 50}{space 3}0.002{col 58}{space 4} .2370118{col 71}{space 3} 1.095921
{txt}{space 16} {c |}
{space 8}race_eth {c |}
{space 10}Black  {c |}{col 18}{res}{space 2} .2914784{col 30}{space 2} .6034788{col 41}{space 1}    0.48{col 50}{space 3}0.629{col 58}{space 4}-.8913183{col 71}{space 3} 1.474275
{txt}{space 7}Hispanic  {c |}{col 18}{res}{space 2}-.7941196{col 30}{space 2}  .453149{col 41}{space 1}   -1.75{col 50}{space 3}0.080{col 58}{space 4}-1.682275{col 71}{space 3} .0940362
{txt}{space 10}Other  {c |}{col 18}{res}{space 2}-.2921017{col 30}{space 2} .7103225{col 41}{space 1}   -0.41{col 50}{space 3}0.681{col 58}{space 4}-1.684308{col 71}{space 3} 1.100105
{txt}{space 16} {c |}
{space 13}age {c |}{col 18}{res}{space 2}-.0076584{col 30}{space 2} .0072583{col 41}{space 1}   -1.06{col 50}{space 3}0.291{col 58}{space 4}-.0218844{col 71}{space 3} .0065675
{txt}{space 12}educ {c |}{col 18}{res}{space 2} .1292674{col 30}{space 2} .1178402{col 41}{space 1}    1.10{col 50}{space 3}0.273{col 58}{space 4}-.1016951{col 71}{space 3} .3602298
{txt}{space 10}income {c |}{col 18}{res}{space 2}-.0333701{col 30}{space 2} .0257424{col 41}{space 1}   -1.30{col 50}{space 3}0.195{col 58}{space 4}-.0838242{col 71}{space 3} .0170841
{txt}{space 16} {c |}
{space 9}marital {c |}
{space 8}Married  {c |}{col 18}{res}{space 2} .1089111{col 30}{space 2}  .261973{col 41}{space 1}    0.42{col 50}{space 3}0.678{col 58}{space 4}-.4045467{col 71}{space 3} .6223688
{txt}{space 7}evaluate1 {c |}{col 18}{res}{space 2} .4442719{col 30}{space 2} .3072712{col 41}{space 1}    1.45{col 50}{space 3}0.148{col 58}{space 4}-.1579686{col 71}{space 3} 1.046512
{txt}{space 12}nfc1 {c |}{col 18}{res}{space 2}-.3390324{col 30}{space 2} .1766709{col 41}{space 1}   -1.92{col 50}{space 3}0.055{col 58}{space 4} -.685301{col 71}{space 3} .0072361
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
           /cut1 {c |}{col 18}{res}{space 2}-2.499004{col 30}{space 2} 1.208831{col 58}{space 4}-4.868268{col 71}{space 3}-.1297388
{txt}           /cut2 {c |}{col 18}{res}{space 2}-.9186396{col 30}{space 2} 1.131581{col 58}{space 4}-3.136497{col 71}{space 3} 1.299218
{txt}           /cut3 {c |}{col 18}{res}{space 2} .4014948{col 30}{space 2} 1.119536{col 58}{space 4}-1.792755{col 71}{space 3} 2.595745
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est6{txt} stored)

{com}. 
. esttab using 2008NOV_ALTMEASURES_PARTISANSHIP.rtf, onecell nobaselevels replace label pr2 aic bic se star(+ 0.10 * 0.05 ** 0.01) ///
>         mtitles("Original Measure" "(/D+A)" "Weigh: by Disc. Interest" ///
>                 "Weigh: by Tie Strength" "Weighted by Gen Dis" "Gen Disagree")  ///
>         title({c -(}\b Table XX.{c )-} "Alternative Measures of Disagreement - 2008-2009 ANES (November)") ///
>         rename(disagree_total Disagreement disagree_avg "Disagreement" disagree_total_int Disagreement disagree_total_close Disagreement ///
>                                 disagree_total_weight Disagreement gendiff Disagreement) 
{res}{txt}(output written to {browse  `"2008NOV_ALTMEASURES_PARTISANSHIP.rtf"'})

{com}. 
. eststo clear
{txt}
{com}. 
. *W17*
. eststo clear
{txt}
{com}. foreach var in disagree_total disagree_avg disagree_total_int disagree_total_close  ///
>         disagree_total_weight gendiff  {c -(}
{txt}  2{com}. eststo: ologit pid_str17_full `var' numgiven1 network_interest i.pid_21 interest_w1 ///
>                         i.gender i.race age educ income i.marital evaluate1 nfc1 [pweight= WGTC17]
{txt}  3{com}.                         {c )-}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-780.06906}  
Iteration 1:{space 3}log pseudolikelihood = {res:-711.11431}  
Iteration 2:{space 3}log pseudolikelihood = {res:-710.09872}  
Iteration 3:{space 3}log pseudolikelihood = {res:-710.09533}  
Iteration 4:{space 3}log pseudolikelihood = {res:-710.09533}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       669
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     58.58
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-710.09533{txt}{col 51}Pseudo R2{col 67}= {res}    0.0897

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}  pid_str17_full{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}disagree_total {c |}{col 18}{res}{space 2} -.399169{col 30}{space 2}  .067456{col 41}{space 1}   -5.92{col 50}{space 3}0.000{col 58}{space 4}-.5313804{col 71}{space 3}-.2669576
{txt}{space 7}numgiven1 {c |}{col 18}{res}{space 2} .1330427{col 30}{space 2} .2449651{col 41}{space 1}    0.54{col 50}{space 3}0.587{col 58}{space 4}  -.34708{col 71}{space 3} .6131654
{txt}network_interest {c |}{col 18}{res}{space 2} .0453198{col 30}{space 2} .1287618{col 41}{space 1}    0.35{col 50}{space 3}0.725{col 58}{space 4}-.2070487{col 71}{space 3} .2976883
{txt}{space 16} {c |}
{space 10}pid_21 {c |}
{space 7}Democrat  {c |}{col 18}{res}{space 2} .4718846{col 30}{space 2} .2398266{col 41}{space 1}    1.97{col 50}{space 3}0.049{col 58}{space 4}  .001833{col 71}{space 3} .9419362
{txt}{space 5}interest_w1 {c |}{col 18}{res}{space 2} .1665101{col 30}{space 2} .1367427{col 41}{space 1}    1.22{col 50}{space 3}0.223{col 58}{space 4}-.1015007{col 71}{space 3} .4345209
{txt}{space 16} {c |}
{space 10}gender {c |}
{space 9}Female  {c |}{col 18}{res}{space 2} .4796758{col 30}{space 2} .2486003{col 41}{space 1}    1.93{col 50}{space 3}0.054{col 58}{space 4}-.0075718{col 71}{space 3} .9669234
{txt}{space 16} {c |}
{space 8}race_eth {c |}
{space 10}Black  {c |}{col 18}{res}{space 2}  .234038{col 30}{space 2} .6979299{col 41}{space 1}    0.34{col 50}{space 3}0.737{col 58}{space 4}-1.133879{col 71}{space 3} 1.601955
{txt}{space 7}Hispanic  {c |}{col 18}{res}{space 2}-.2087881{col 30}{space 2} .5131791{col 41}{space 1}   -0.41{col 50}{space 3}0.684{col 58}{space 4}-1.214601{col 71}{space 3} .7970244
{txt}{space 10}Other  {c |}{col 18}{res}{space 2}-.0609236{col 30}{space 2} .6464678{col 41}{space 1}   -0.09{col 50}{space 3}0.925{col 58}{space 4}-1.327977{col 71}{space 3}  1.20613
{txt}{space 16} {c |}
{space 13}age {c |}{col 18}{res}{space 2} .0008469{col 30}{space 2} .0082321{col 41}{space 1}    0.10{col 50}{space 3}0.918{col 58}{space 4}-.0152877{col 71}{space 3} .0169815
{txt}{space 12}educ {c |}{col 18}{res}{space 2}-.1572061{col 30}{space 2}  .124257{col 41}{space 1}   -1.27{col 50}{space 3}0.206{col 58}{space 4}-.4007453{col 71}{space 3} .0863332
{txt}{space 10}income {c |}{col 18}{res}{space 2} .0073674{col 30}{space 2} .0343999{col 41}{space 1}    0.21{col 50}{space 3}0.830{col 58}{space 4}-.0600551{col 71}{space 3} .0747899
{txt}{space 16} {c |}
{space 9}marital {c |}
{space 8}Married  {c |}{col 18}{res}{space 2} .0100522{col 30}{space 2} .2865855{col 41}{space 1}    0.04{col 50}{space 3}0.972{col 58}{space 4}-.5516451{col 71}{space 3} .5717494
{txt}{space 7}evaluate1 {c |}{col 18}{res}{space 2}-.0862634{col 30}{space 2} .3230269{col 41}{space 1}   -0.27{col 50}{space 3}0.789{col 58}{space 4}-.7193843{col 71}{space 3} .5468576
{txt}{space 12}nfc1 {c |}{col 18}{res}{space 2}-.0628431{col 30}{space 2} .1858105{col 41}{space 1}   -0.34{col 50}{space 3}0.735{col 58}{space 4}-.4270249{col 71}{space 3} .3013387
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
           /cut1 {c |}{col 18}{res}{space 2}-1.453811{col 30}{space 2} 1.165896{col 58}{space 4}-3.738925{col 71}{space 3} .8313032
{txt}           /cut2 {c |}{col 18}{res}{space 2} .3464632{col 30}{space 2}   1.1196{col 58}{space 4}-1.847913{col 71}{space 3} 2.540839
{txt}           /cut3 {c |}{col 18}{res}{space 2} 1.987214{col 30}{space 2} 1.116151{col 58}{space 4}-.2004025{col 71}{space 3}  4.17483
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-780.06906}  
Iteration 1:{space 3}log pseudolikelihood = {res:-709.76833}  
Iteration 2:{space 3}log pseudolikelihood = {res:-708.61042}  
Iteration 3:{space 3}log pseudolikelihood = {res:-708.60636}  
Iteration 4:{space 3}log pseudolikelihood = {res:-708.60636}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       669
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     60.40
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-708.60636{txt}{col 51}Pseudo R2{col 67}= {res}    0.0916

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}  pid_str17_full{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}disagree_avg {c |}{col 18}{res}{space 2}-1.104463{col 30}{space 2} .1923312{col 41}{space 1}   -5.74{col 50}{space 3}0.000{col 58}{space 4}-1.481425{col 71}{space 3}-.7275003
{txt}{space 7}numgiven1 {c |}{col 18}{res}{space 2} .0988753{col 30}{space 2} .2601848{col 41}{space 1}    0.38{col 50}{space 3}0.704{col 58}{space 4}-.4110776{col 71}{space 3} .6088281
{txt}network_interest {c |}{col 18}{res}{space 2}  .029287{col 30}{space 2} .1296513{col 41}{space 1}    0.23{col 50}{space 3}0.821{col 58}{space 4}-.2248249{col 71}{space 3} .2833989
{txt}{space 16} {c |}
{space 10}pid_21 {c |}
{space 7}Democrat  {c |}{col 18}{res}{space 2} .4644789{col 30}{space 2} .2426671{col 41}{space 1}    1.91{col 50}{space 3}0.056{col 58}{space 4}-.0111398{col 71}{space 3} .9400976
{txt}{space 5}interest_w1 {c |}{col 18}{res}{space 2} .1763099{col 30}{space 2} .1349201{col 41}{space 1}    1.31{col 50}{space 3}0.191{col 58}{space 4}-.0881287{col 71}{space 3} .4407485
{txt}{space 16} {c |}
{space 10}gender {c |}
{space 9}Female  {c |}{col 18}{res}{space 2} .4849751{col 30}{space 2} .2407508{col 41}{space 1}    2.01{col 50}{space 3}0.044{col 58}{space 4} .0131122{col 71}{space 3} .9568379
{txt}{space 16} {c |}
{space 8}race_eth {c |}
{space 10}Black  {c |}{col 18}{res}{space 2} .2483838{col 30}{space 2} .6882378{col 41}{space 1}    0.36{col 50}{space 3}0.718{col 58}{space 4}-1.100537{col 71}{space 3} 1.597305
{txt}{space 7}Hispanic  {c |}{col 18}{res}{space 2}-.1649163{col 30}{space 2} .4671054{col 41}{space 1}   -0.35{col 50}{space 3}0.724{col 58}{space 4}-1.080426{col 71}{space 3} .7505935
{txt}{space 10}Other  {c |}{col 18}{res}{space 2}-.0817612{col 30}{space 2} .6319736{col 41}{space 1}   -0.13{col 50}{space 3}0.897{col 58}{space 4}-1.320407{col 71}{space 3} 1.156884
{txt}{space 16} {c |}
{space 13}age {c |}{col 18}{res}{space 2} .0017738{col 30}{space 2} .0079868{col 41}{space 1}    0.22{col 50}{space 3}0.824{col 58}{space 4}  -.01388{col 71}{space 3} .0174276
{txt}{space 12}educ {c |}{col 18}{res}{space 2} -.156021{col 30}{space 2} .1249939{col 41}{space 1}   -1.25{col 50}{space 3}0.212{col 58}{space 4}-.4010046{col 71}{space 3} .0889625
{txt}{space 10}income {c |}{col 18}{res}{space 2} .0048915{col 30}{space 2} .0338069{col 41}{space 1}    0.14{col 50}{space 3}0.885{col 58}{space 4}-.0613688{col 71}{space 3} .0711518
{txt}{space 16} {c |}
{space 9}marital {c |}
{space 8}Married  {c |}{col 18}{res}{space 2} .0366488{col 30}{space 2} .2820829{col 41}{space 1}    0.13{col 50}{space 3}0.897{col 58}{space 4}-.5162236{col 71}{space 3} .5895211
{txt}{space 7}evaluate1 {c |}{col 18}{res}{space 2}-.0608594{col 30}{space 2} .3188204{col 41}{space 1}   -0.19{col 50}{space 3}0.849{col 58}{space 4} -.685736{col 71}{space 3} .5640171
{txt}{space 12}nfc1 {c |}{col 18}{res}{space 2}-.0761857{col 30}{space 2}   .18374{col 41}{space 1}   -0.41{col 50}{space 3}0.678{col 58}{space 4}-.4363096{col 71}{space 3} .2839381
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
           /cut1 {c |}{col 18}{res}{space 2}-1.560513{col 30}{space 2} 1.084847{col 58}{space 4}-3.686774{col 71}{space 3} .5657478
{txt}           /cut2 {c |}{col 18}{res}{space 2} .2518038{col 30}{space 2} 1.055314{col 58}{space 4}-1.816573{col 71}{space 3}  2.32018
{txt}           /cut3 {c |}{col 18}{res}{space 2} 1.896657{col 30}{space 2}  1.03556{col 58}{space 4} -.133003{col 71}{space 3} 3.926316
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-780.06906}  
Iteration 1:{space 3}log pseudolikelihood = {res:-711.62551}  
Iteration 2:{space 3}log pseudolikelihood = {res:-710.53636}  
Iteration 3:{space 3}log pseudolikelihood = {res:-710.53294}  
Iteration 4:{space 3}log pseudolikelihood = {res:-710.53294}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       669
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     60.46
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-710.53294{txt}{col 51}Pseudo R2{col 67}= {res}    0.0891

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}    pid_str17_full{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      z{col 52}   P>|z|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
disagree_total_int {c |}{col 20}{res}{space 2}-.1056744{col 32}{space 2} .0176285{col 43}{space 1}   -5.99{col 52}{space 3}0.000{col 60}{space 4}-.1402257{col 73}{space 3}-.0711231
{txt}{space 9}numgiven1 {c |}{col 20}{res}{space 2} .1337594{col 32}{space 2} .2467225{col 43}{space 1}    0.54{col 52}{space 3}0.588{col 60}{space 4}-.3498078{col 73}{space 3} .6173265
{txt}{space 2}network_interest {c |}{col 20}{res}{space 2}-.0420197{col 32}{space 2} .1283193{col 43}{space 1}   -0.33{col 52}{space 3}0.743{col 60}{space 4}-.2935209{col 73}{space 3} .2094815
{txt}{space 18} {c |}
{space 12}pid_21 {c |}
{space 9}Democrat  {c |}{col 20}{res}{space 2} .4500339{col 32}{space 2} .2386968{col 43}{space 1}    1.89{col 52}{space 3}0.059{col 60}{space 4}-.0178033{col 73}{space 3}  .917871
{txt}{space 7}interest_w1 {c |}{col 20}{res}{space 2} .1433193{col 32}{space 2} .1389971{col 43}{space 1}    1.03{col 52}{space 3}0.302{col 60}{space 4}  -.12911{col 73}{space 3} .4157485
{txt}{space 18} {c |}
{space 12}gender {c |}
{space 11}Female  {c |}{col 20}{res}{space 2}  .490858{col 32}{space 2} .2462401{col 43}{space 1}    1.99{col 52}{space 3}0.046{col 60}{space 4} .0082363{col 73}{space 3} .9734797
{txt}{space 18} {c |}
{space 10}race_eth {c |}
{space 12}Black  {c |}{col 20}{res}{space 2} .2155504{col 32}{space 2} .6985657{col 43}{space 1}    0.31{col 52}{space 3}0.758{col 60}{space 4}-1.153613{col 73}{space 3} 1.584714
{txt}{space 9}Hispanic  {c |}{col 20}{res}{space 2}-.2096541{col 32}{space 2} .5039895{col 43}{space 1}   -0.42{col 52}{space 3}0.677{col 60}{space 4}-1.197455{col 73}{space 3} .7781472
{txt}{space 12}Other  {c |}{col 20}{res}{space 2}   -.1723{col 32}{space 2} .6127953{col 43}{space 1}   -0.28{col 52}{space 3}0.779{col 60}{space 4}-1.373357{col 73}{space 3} 1.028757
{txt}{space 18} {c |}
{space 15}age {c |}{col 20}{res}{space 2} .0027166{col 32}{space 2} .0083096{col 43}{space 1}    0.33{col 52}{space 3}0.744{col 60}{space 4}-.0135698{col 73}{space 3}  .019003
{txt}{space 14}educ {c |}{col 20}{res}{space 2}-.1730592{col 32}{space 2} .1273979{col 43}{space 1}   -1.36{col 52}{space 3}0.174{col 60}{space 4}-.4227545{col 73}{space 3} .0766361
{txt}{space 12}income {c |}{col 20}{res}{space 2}  .013013{col 32}{space 2} .0352489{col 43}{space 1}    0.37{col 52}{space 3}0.712{col 60}{space 4}-.0560736{col 73}{space 3} .0820995
{txt}{space 18} {c |}
{space 11}marital {c |}
{space 10}Married  {c |}{col 20}{res}{space 2} .0131702{col 32}{space 2} .2860688{col 43}{space 1}    0.05{col 52}{space 3}0.963{col 60}{space 4}-.5475144{col 73}{space 3} .5738548
{txt}{space 9}evaluate1 {c |}{col 20}{res}{space 2}-.0712309{col 32}{space 2} .3196065{col 43}{space 1}   -0.22{col 52}{space 3}0.824{col 60}{space 4}-.6976481{col 73}{space 3} .5551863
{txt}{space 14}nfc1 {c |}{col 20}{res}{space 2}-.0390725{col 32}{space 2} .1886883{col 43}{space 1}   -0.21{col 52}{space 3}0.836{col 60}{space 4}-.4088948{col 73}{space 3} .3307498
{txt}{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
             /cut1 {c |}{col 20}{res}{space 2}-1.696215{col 32}{space 2} 1.190152{col 60}{space 4}-4.028871{col 73}{space 3}  .636441
{txt}             /cut2 {c |}{col 20}{res}{space 2} .1087874{col 32}{space 2} 1.137313{col 60}{space 4}-2.120306{col 73}{space 3} 2.337881
{txt}             /cut3 {c |}{col 20}{res}{space 2} 1.748191{col 32}{space 2} 1.136054{col 60}{space 4}-.4784343{col 73}{space 3} 3.974816
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-780.06906}  
Iteration 1:{space 3}log pseudolikelihood = {res:-708.97652}  
Iteration 2:{space 3}log pseudolikelihood = {res:-707.90886}  
Iteration 3:{space 3}log pseudolikelihood = {res:-707.90528}  
Iteration 4:{space 3}log pseudolikelihood = {res:-707.90528}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       669
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     68.19
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-707.90528{txt}{col 51}Pseudo R2{col 67}= {res}    0.0925

{txt}{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}      pid_str17_full{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
disagree_total_close {c |}{col 22}{res}{space 2}-.1001093{col 34}{space 2} .0158544{col 45}{space 1}   -6.31{col 54}{space 3}0.000{col 62}{space 4}-.1311833{col 75}{space 3}-.0690354
{txt}{space 11}numgiven1 {c |}{col 22}{res}{space 2} .1579748{col 34}{space 2} .2459539{col 45}{space 1}    0.64{col 54}{space 3}0.521{col 62}{space 4}-.3240859{col 75}{space 3} .6400355
{txt}{space 4}network_interest {c |}{col 22}{res}{space 2} .0214491{col 34}{space 2} .1298349{col 45}{space 1}    0.17{col 54}{space 3}0.869{col 62}{space 4}-.2330227{col 75}{space 3} .2759208
{txt}{space 20} {c |}
{space 14}pid_21 {c |}
{space 11}Democrat  {c |}{col 22}{res}{space 2} .4649652{col 34}{space 2} .2407727{col 45}{space 1}    1.93{col 54}{space 3}0.053{col 62}{space 4}-.0069407{col 75}{space 3} .9368711
{txt}{space 9}interest_w1 {c |}{col 22}{res}{space 2} .1902056{col 34}{space 2} .1373688{col 45}{space 1}    1.38{col 54}{space 3}0.166{col 62}{space 4}-.0790323{col 75}{space 3} .4594436
{txt}{space 20} {c |}
{space 14}gender {c |}
{space 13}Female  {c |}{col 22}{res}{space 2} .4697265{col 34}{space 2}  .249668{col 45}{space 1}    1.88{col 54}{space 3}0.060{col 62}{space 4}-.0196139{col 75}{space 3} .9590668
{txt}{space 20} {c |}
{space 12}race_eth {c |}
{space 14}Black  {c |}{col 22}{res}{space 2} .2457925{col 34}{space 2} .6940541{col 45}{space 1}    0.35{col 54}{space 3}0.723{col 62}{space 4}-1.114529{col 75}{space 3} 1.606114
{txt}{space 11}Hispanic  {c |}{col 22}{res}{space 2}-.1838941{col 34}{space 2} .5286223{col 45}{space 1}   -0.35{col 54}{space 3}0.728{col 62}{space 4}-1.219975{col 75}{space 3} .8521865
{txt}{space 14}Other  {c |}{col 22}{res}{space 2}-.0956093{col 34}{space 2}  .676733{col 45}{space 1}   -0.14{col 54}{space 3}0.888{col 62}{space 4}-1.421982{col 75}{space 3} 1.230763
{txt}{space 20} {c |}
{space 17}age {c |}{col 22}{res}{space 2} .0013116{col 34}{space 2} .0081926{col 45}{space 1}    0.16{col 54}{space 3}0.873{col 62}{space 4}-.0147456{col 75}{space 3} .0173687
{txt}{space 16}educ {c |}{col 22}{res}{space 2}-.1665539{col 34}{space 2} .1229364{col 45}{space 1}   -1.35{col 54}{space 3}0.175{col 62}{space 4}-.4075047{col 75}{space 3}  .074397
{txt}{space 14}income {c |}{col 22}{res}{space 2} .0109714{col 34}{space 2} .0333692{col 45}{space 1}    0.33{col 54}{space 3}0.742{col 62}{space 4} -.054431{col 75}{space 3} .0763737
{txt}{space 20} {c |}
{space 13}marital {c |}
{space 12}Married  {c |}{col 22}{res}{space 2} .0150455{col 34}{space 2} .2890647{col 45}{space 1}    0.05{col 54}{space 3}0.958{col 62}{space 4} -.551511{col 75}{space 3} .5816019
{txt}{space 11}evaluate1 {c |}{col 22}{res}{space 2}-.0861734{col 34}{space 2} .3313456{col 45}{space 1}   -0.26{col 54}{space 3}0.795{col 62}{space 4} -.735599{col 75}{space 3} .5632521
{txt}{space 16}nfc1 {c |}{col 22}{res}{space 2}-.0712084{col 34}{space 2} .1851351{col 45}{space 1}   -0.38{col 54}{space 3}0.701{col 62}{space 4}-.4340666{col 75}{space 3} .2916497
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
               /cut1 {c |}{col 22}{res}{space 2}-1.336419{col 34}{space 2} 1.160866{col 62}{space 4}-3.611675{col 75}{space 3}  .938837
{txt}               /cut2 {c |}{col 22}{res}{space 2} .4678283{col 34}{space 2} 1.111994{col 62}{space 4}-1.711641{col 75}{space 3} 2.647297
{txt}               /cut3 {c |}{col 22}{res}{space 2} 2.118801{col 34}{space 2} 1.107239{col 62}{space 4}-.0513479{col 75}{space 3}  4.28895
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-780.06906}  
Iteration 1:{space 3}log pseudolikelihood = {res:-705.84129}  
Iteration 2:{space 3}log pseudolikelihood = {res:-704.77534}  
Iteration 3:{space 3}log pseudolikelihood = {res: -704.7716}  
Iteration 4:{space 3}log pseudolikelihood = {res: -704.7716}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       669
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     75.90
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res} -704.7716{txt}{col 51}Pseudo R2{col 67}= {res}    0.0965

{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}    Robust
{col 1}       pid_str17_full{col 23}{c |}      Coef.{col 35}   Std. Err.{col 47}      z{col 55}   P>|z|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
disagree_total_weight {c |}{col 23}{res}{space 2}-.1193396{col 35}{space 2} .0173454{col 46}{space 1}   -6.88{col 55}{space 3}0.000{col 63}{space 4} -.153336{col 76}{space 3}-.0853432
{txt}{space 12}numgiven1 {c |}{col 23}{res}{space 2} .0766561{col 35}{space 2} .2488251{col 46}{space 1}    0.31{col 55}{space 3}0.758{col 63}{space 4} -.411032{col 76}{space 3} .5643443
{txt}{space 5}network_interest {c |}{col 23}{res}{space 2} .0225402{col 35}{space 2} .1329943{col 46}{space 1}    0.17{col 55}{space 3}0.865{col 63}{space 4}-.2381239{col 76}{space 3} .2832042
{txt}{space 21} {c |}
{space 15}pid_21 {c |}
{space 12}Democrat  {c |}{col 23}{res}{space 2} .4940353{col 35}{space 2} .2423414{col 46}{space 1}    2.04{col 55}{space 3}0.041{col 63}{space 4}  .019055{col 76}{space 3} .9690157
{txt}{space 10}interest_w1 {c |}{col 23}{res}{space 2} .1862859{col 35}{space 2} .1384262{col 46}{space 1}    1.35{col 55}{space 3}0.178{col 63}{space 4}-.0850244{col 76}{space 3} .4575963
{txt}{space 21} {c |}
{space 15}gender {c |}
{space 14}Female  {c |}{col 23}{res}{space 2} .4954213{col 35}{space 2} .2512155{col 46}{space 1}    1.97{col 55}{space 3}0.049{col 63}{space 4}  .003048{col 76}{space 3} .9877947
{txt}{space 21} {c |}
{space 13}race_eth {c |}
{space 15}Black  {c |}{col 23}{res}{space 2} .1925401{col 35}{space 2} .7079714{col 46}{space 1}    0.27{col 55}{space 3}0.786{col 63}{space 4}-1.195058{col 76}{space 3} 1.580139
{txt}{space 12}Hispanic  {c |}{col 23}{res}{space 2} -.122044{col 35}{space 2} .5189337{col 46}{space 1}   -0.24{col 55}{space 3}0.814{col 63}{space 4}-1.139135{col 76}{space 3} .8950474
{txt}{space 15}Other  {c |}{col 23}{res}{space 2}-.2346852{col 35}{space 2} .5711996{col 46}{space 1}   -0.41{col 55}{space 3}0.681{col 63}{space 4}-1.354216{col 76}{space 3} .8848454
{txt}{space 21} {c |}
{space 18}age {c |}{col 23}{res}{space 2} .0001131{col 35}{space 2} .0084196{col 46}{space 1}    0.01{col 55}{space 3}0.989{col 63}{space 4} -.016389{col 76}{space 3} .0166152
{txt}{space 17}educ {c |}{col 23}{res}{space 2}-.1458759{col 35}{space 2} .1228726{col 46}{space 1}   -1.19{col 55}{space 3}0.235{col 63}{space 4}-.3867018{col 76}{space 3} .0949499
{txt}{space 15}income {c |}{col 23}{res}{space 2} .0071847{col 35}{space 2} .0332424{col 46}{space 1}    0.22{col 55}{space 3}0.829{col 63}{space 4}-.0579693{col 76}{space 3} .0723387
{txt}{space 21} {c |}
{space 14}marital {c |}
{space 13}Married  {c |}{col 23}{res}{space 2} .0541732{col 35}{space 2} .2914447{col 46}{space 1}    0.19{col 55}{space 3}0.853{col 63}{space 4}-.5170479{col 76}{space 3} .6253943
{txt}{space 12}evaluate1 {c |}{col 23}{res}{space 2}-.1141848{col 35}{space 2} .3232654{col 46}{space 1}   -0.35{col 55}{space 3}0.724{col 63}{space 4}-.7477733{col 76}{space 3} .5194038
{txt}{space 17}nfc1 {c |}{col 23}{res}{space 2}-.0427072{col 35}{space 2} .1872404{col 46}{space 1}   -0.23{col 55}{space 3}0.820{col 63}{space 4}-.4096916{col 76}{space 3} .3242772
{txt}{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                /cut1 {c |}{col 23}{res}{space 2}-1.383149{col 35}{space 2} 1.190849{col 63}{space 4} -3.71717{col 76}{space 3} .9508715
{txt}                /cut2 {c |}{col 23}{res}{space 2}  .417854{col 35}{space 2} 1.139567{col 63}{space 4}-1.815656{col 76}{space 3} 2.651364
{txt}                /cut3 {c |}{col 23}{res}{space 2} 2.078487{col 35}{space 2} 1.135025{col 63}{space 4}-.1461206{col 76}{space 3} 4.303095
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est5{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-780.77806}  
Iteration 1:{space 3}log pseudolikelihood = {res: -734.2093}  
Iteration 2:{space 3}log pseudolikelihood = {res: -733.8189}  
Iteration 3:{space 3}log pseudolikelihood = {res:-733.81855}  
Iteration 4:{space 3}log pseudolikelihood = {res:-733.81855}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       670
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     47.16
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-733.81855{txt}{col 51}Pseudo R2{col 67}= {res}    0.0601

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}  pid_str17_full{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}gendiff {c |}{col 18}{res}{space 2}-.6154888{col 30}{space 2} .1621916{col 41}{space 1}   -3.79{col 50}{space 3}0.000{col 58}{space 4}-.9333784{col 71}{space 3}-.2975992
{txt}{space 7}numgiven1 {c |}{col 18}{res}{space 2} .3613673{col 30}{space 2} .2275255{col 41}{space 1}    1.59{col 50}{space 3}0.112{col 58}{space 4}-.0845745{col 71}{space 3} .8073092
{txt}network_interest {c |}{col 18}{res}{space 2} .0111484{col 30}{space 2} .1289315{col 41}{space 1}    0.09{col 50}{space 3}0.931{col 58}{space 4}-.2415528{col 71}{space 3} .2638496
{txt}{space 16} {c |}
{space 10}pid_21 {c |}
{space 7}Democrat  {c |}{col 18}{res}{space 2} .4236346{col 30}{space 2} .2308611{col 41}{space 1}    1.84{col 50}{space 3}0.067{col 58}{space 4}-.0288449{col 71}{space 3} .8761141
{txt}{space 5}interest_w1 {c |}{col 18}{res}{space 2} .1985445{col 30}{space 2} .1323335{col 41}{space 1}    1.50{col 50}{space 3}0.134{col 58}{space 4}-.0608244{col 71}{space 3} .4579134
{txt}{space 16} {c |}
{space 10}gender {c |}
{space 9}Female  {c |}{col 18}{res}{space 2} .6633986{col 30}{space 2} .2381473{col 41}{space 1}    2.79{col 50}{space 3}0.005{col 58}{space 4} .1966385{col 71}{space 3} 1.130159
{txt}{space 16} {c |}
{space 8}race_eth {c |}
{space 10}Black  {c |}{col 18}{res}{space 2} .4493119{col 30}{space 2} .5985706{col 41}{space 1}    0.75{col 50}{space 3}0.453{col 58}{space 4}-.7238649{col 71}{space 3} 1.622489
{txt}{space 7}Hispanic  {c |}{col 18}{res}{space 2} -.271614{col 30}{space 2} .5431966{col 41}{space 1}   -0.50{col 50}{space 3}0.617{col 58}{space 4} -1.33626{col 71}{space 3} .7930317
{txt}{space 10}Other  {c |}{col 18}{res}{space 2}-.7670022{col 30}{space 2} .6331295{col 41}{space 1}   -1.21{col 50}{space 3}0.226{col 58}{space 4}-2.007913{col 71}{space 3} .4739087
{txt}{space 16} {c |}
{space 13}age {c |}{col 18}{res}{space 2}-.0010202{col 30}{space 2} .0080394{col 41}{space 1}   -0.13{col 50}{space 3}0.899{col 58}{space 4}-.0167771{col 71}{space 3} .0147368
{txt}{space 12}educ {c |}{col 18}{res}{space 2}-.0612635{col 30}{space 2} .1314837{col 41}{space 1}   -0.47{col 50}{space 3}0.641{col 58}{space 4}-.3189667{col 71}{space 3} .1964398
{txt}{space 10}income {c |}{col 18}{res}{space 2}-.0050639{col 30}{space 2} .0347877{col 41}{space 1}   -0.15{col 50}{space 3}0.884{col 58}{space 4}-.0732464{col 71}{space 3} .0631187
{txt}{space 16} {c |}
{space 9}marital {c |}
{space 8}Married  {c |}{col 18}{res}{space 2} .0746243{col 30}{space 2} .2748638{col 41}{space 1}    0.27{col 50}{space 3}0.786{col 58}{space 4}-.4640988{col 71}{space 3} .6133474
{txt}{space 7}evaluate1 {c |}{col 18}{res}{space 2} .0917067{col 30}{space 2} .3208198{col 41}{space 1}    0.29{col 50}{space 3}0.775{col 58}{space 4}-.5370886{col 71}{space 3}  .720502
{txt}{space 12}nfc1 {c |}{col 18}{res}{space 2}-.0640503{col 30}{space 2}  .182574{col 41}{space 1}   -0.35{col 50}{space 3}0.726{col 58}{space 4}-.4218888{col 71}{space 3} .2937882
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
           /cut1 {c |}{col 18}{res}{space 2}-2.100592{col 30}{space 2} 1.315559{col 58}{space 4}-4.679039{col 71}{space 3} .4778557
{txt}           /cut2 {c |}{col 18}{res}{space 2}-.3769052{col 30}{space 2} 1.239866{col 58}{space 4}-2.806999{col 71}{space 3} 2.053188
{txt}           /cut3 {c |}{col 18}{res}{space 2} 1.178199{col 30}{space 2} 1.233927{col 58}{space 4}-1.240254{col 71}{space 3} 3.596652
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est6{txt} stored)

{com}.                         
. esttab using 2008MAY_ALTMEASURES_PARTISANSHIP.rtf, onecell nobaselevels replace label pr2 aic bic se star(+ 0.10 * 0.05 ** 0.01) ///
>         mtitles("Original Measure" "(/D+A)" "Weigh: by Disc. Interest" ///
>                 "Weigh: by Tie Strength" "Weighted by Gen Dis" "Gen Disagree")  ///
>         title({c -(}\b Table XX.{c )-} "Alternative Measures of Disagreement - 2008-2009 ANES (May 2009)") ///
>         rename(disagree_total Disagreement disagree_avg "Disagreement" disagree_total_int Disagreement disagree_total_close Disagreement ///
>                                 disagree_total_weight Disagreement gendiff Disagreement) 
{res}{txt}(output written to {browse  `"2008MAY_ALTMEASURES_PARTISANSHIP.rtf"'})

{com}. 
. eststo clear
{txt}
{com}.                         
. *W19
. eststo clear
{txt}
{com}. foreach var in disagree_total disagree_avg disagree_total_int disagree_total_close  ///
>         disagree_total_weight gendiff  {c -(}
{txt}  2{com}. eststo: ologit pid_str19_full `var' numgiven1 network_interest i.pid_21 interest_w1 i.gender ////
>                         i.race age educ income i.marital evaluate1 nfc1 [pweight= WGTC19]
{txt}  3{com}.         {c )-}       

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -730.5074}  
Iteration 1:{space 3}log pseudolikelihood = {res:-665.88026}  
Iteration 2:{space 3}log pseudolikelihood = {res:-664.77938}  
Iteration 3:{space 3}log pseudolikelihood = {res:-664.77583}  
Iteration 4:{space 3}log pseudolikelihood = {res:-664.77583}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       620
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     54.43
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-664.77583{txt}{col 51}Pseudo R2{col 67}= {res}    0.0900

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}  pid_str19_full{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}disagree_total {c |}{col 18}{res}{space 2}-.3613159{col 30}{space 2} .0655949{col 41}{space 1}   -5.51{col 50}{space 3}0.000{col 58}{space 4}-.4898795{col 71}{space 3}-.2327523
{txt}{space 7}numgiven1 {c |}{col 18}{res}{space 2}-.3306014{col 30}{space 2} .2095661{col 41}{space 1}   -1.58{col 50}{space 3}0.115{col 58}{space 4}-.7413435{col 71}{space 3} .0801407
{txt}network_interest {c |}{col 18}{res}{space 2}-.0794841{col 30}{space 2} .1740642{col 41}{space 1}   -0.46{col 50}{space 3}0.648{col 58}{space 4}-.4206438{col 71}{space 3} .2616755
{txt}{space 16} {c |}
{space 10}pid_21 {c |}
{space 7}Democrat  {c |}{col 18}{res}{space 2} .4346757{col 30}{space 2} .2483933{col 41}{space 1}    1.75{col 50}{space 3}0.080{col 58}{space 4}-.0521662{col 71}{space 3} .9215176
{txt}{space 5}interest_w1 {c |}{col 18}{res}{space 2} .3678286{col 30}{space 2} .1581241{col 41}{space 1}    2.33{col 50}{space 3}0.020{col 58}{space 4} .0579111{col 71}{space 3} .6777461
{txt}{space 16} {c |}
{space 10}gender {c |}
{space 9}Female  {c |}{col 18}{res}{space 2} .5237259{col 30}{space 2} .2496844{col 41}{space 1}    2.10{col 50}{space 3}0.036{col 58}{space 4} .0343535{col 71}{space 3} 1.013098
{txt}{space 16} {c |}
{space 8}race_eth {c |}
{space 10}Black  {c |}{col 18}{res}{space 2} 1.162687{col 30}{space 2} .6974814{col 41}{space 1}    1.67{col 50}{space 3}0.096{col 58}{space 4}-.2043511{col 71}{space 3} 2.529726
{txt}{space 7}Hispanic  {c |}{col 18}{res}{space 2}-.0734907{col 30}{space 2} .5094504{col 41}{space 1}   -0.14{col 50}{space 3}0.885{col 58}{space 4}-1.071995{col 71}{space 3} .9250138
{txt}{space 10}Other  {c |}{col 18}{res}{space 2} .4576229{col 30}{space 2} .7722625{col 41}{space 1}    0.59{col 50}{space 3}0.553{col 58}{space 4}-1.055984{col 71}{space 3}  1.97123
{txt}{space 16} {c |}
{space 13}age {c |}{col 18}{res}{space 2} .0029367{col 30}{space 2} .0091667{col 41}{space 1}    0.32{col 50}{space 3}0.749{col 58}{space 4}-.0150297{col 71}{space 3} .0209032
{txt}{space 12}educ {c |}{col 18}{res}{space 2} .0243732{col 30}{space 2} .1441631{col 41}{space 1}    0.17{col 50}{space 3}0.866{col 58}{space 4}-.2581814{col 71}{space 3} .3069277
{txt}{space 10}income {c |}{col 18}{res}{space 2} .0230675{col 30}{space 2} .0416098{col 41}{space 1}    0.55{col 50}{space 3}0.579{col 58}{space 4}-.0584862{col 71}{space 3} .1046211
{txt}{space 16} {c |}
{space 9}marital {c |}
{space 8}Married  {c |}{col 18}{res}{space 2}-.1536813{col 30}{space 2} .3304567{col 41}{space 1}   -0.47{col 50}{space 3}0.642{col 58}{space 4}-.8013646{col 71}{space 3}  .494002
{txt}{space 7}evaluate1 {c |}{col 18}{res}{space 2} .0633145{col 30}{space 2} .3387474{col 41}{space 1}    0.19{col 50}{space 3}0.852{col 58}{space 4}-.6006183{col 71}{space 3} .7272472
{txt}{space 12}nfc1 {c |}{col 18}{res}{space 2}-.2106132{col 30}{space 2} .2051549{col 41}{space 1}   -1.03{col 50}{space 3}0.305{col 58}{space 4}-.6127093{col 71}{space 3}  .191483
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
           /cut1 {c |}{col 18}{res}{space 2}-1.392034{col 30}{space 2} 1.070024{col 58}{space 4}-3.489243{col 71}{space 3} .7051748
{txt}           /cut2 {c |}{col 18}{res}{space 2} .2379529{col 30}{space 2} 1.030701{col 58}{space 4}-1.782184{col 71}{space 3}  2.25809
{txt}           /cut3 {c |}{col 18}{res}{space 2} 1.777629{col 30}{space 2} 1.023587{col 58}{space 4}-.2285645{col 71}{space 3} 3.783823
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -730.5074}  
Iteration 1:{space 3}log pseudolikelihood = {res:-664.64631}  
Iteration 2:{space 3}log pseudolikelihood = {res:-663.58638}  
Iteration 3:{space 3}log pseudolikelihood = {res:-663.58307}  
Iteration 4:{space 3}log pseudolikelihood = {res:-663.58307}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       620
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     57.37
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-663.58307{txt}{col 51}Pseudo R2{col 67}= {res}    0.0916

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}  pid_str19_full{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}disagree_avg {c |}{col 18}{res}{space 2}-.9856077{col 30}{space 2} .1738658{col 41}{space 1}   -5.67{col 50}{space 3}0.000{col 58}{space 4}-1.326378{col 71}{space 3}-.6448371
{txt}{space 7}numgiven1 {c |}{col 18}{res}{space 2}-.3710053{col 30}{space 2} .2258317{col 41}{space 1}   -1.64{col 50}{space 3}0.100{col 58}{space 4}-.8136272{col 71}{space 3} .0716166
{txt}network_interest {c |}{col 18}{res}{space 2}-.1021528{col 30}{space 2} .1743927{col 41}{space 1}   -0.59{col 50}{space 3}0.558{col 58}{space 4}-.4439563{col 71}{space 3} .2396507
{txt}{space 16} {c |}
{space 10}pid_21 {c |}
{space 7}Democrat  {c |}{col 18}{res}{space 2} .4079216{col 30}{space 2} .2496638{col 41}{space 1}    1.63{col 50}{space 3}0.102{col 58}{space 4}-.0814105{col 71}{space 3} .8972536
{txt}{space 5}interest_w1 {c |}{col 18}{res}{space 2} .3855623{col 30}{space 2} .1591891{col 41}{space 1}    2.42{col 50}{space 3}0.015{col 58}{space 4} .0735574{col 71}{space 3} .6975671
{txt}{space 16} {c |}
{space 10}gender {c |}
{space 9}Female  {c |}{col 18}{res}{space 2} .5143907{col 30}{space 2} .2498404{col 41}{space 1}    2.06{col 50}{space 3}0.040{col 58}{space 4} .0247126{col 71}{space 3} 1.004069
{txt}{space 16} {c |}
{space 8}race_eth {c |}
{space 10}Black  {c |}{col 18}{res}{space 2} 1.226432{col 30}{space 2} .6880484{col 41}{space 1}    1.78{col 50}{space 3}0.075{col 58}{space 4}-.1221179{col 71}{space 3} 2.574982
{txt}{space 7}Hispanic  {c |}{col 18}{res}{space 2}-.0299414{col 30}{space 2} .5140857{col 41}{space 1}   -0.06{col 50}{space 3}0.954{col 58}{space 4}-1.037531{col 71}{space 3} .9776481
{txt}{space 10}Other  {c |}{col 18}{res}{space 2} .4355925{col 30}{space 2} .7452427{col 41}{space 1}    0.58{col 50}{space 3}0.559{col 58}{space 4}-1.025056{col 71}{space 3} 1.896241
{txt}{space 16} {c |}
{space 13}age {c |}{col 18}{res}{space 2} .0038939{col 30}{space 2} .0089627{col 41}{space 1}    0.43{col 50}{space 3}0.664{col 58}{space 4}-.0136726{col 71}{space 3} .0214604
{txt}{space 12}educ {c |}{col 18}{res}{space 2} .0187025{col 30}{space 2} .1427578{col 41}{space 1}    0.13{col 50}{space 3}0.896{col 58}{space 4}-.2610976{col 71}{space 3} .2985026
{txt}{space 10}income {c |}{col 18}{res}{space 2} .0204477{col 30}{space 2} .0408081{col 41}{space 1}    0.50{col 50}{space 3}0.616{col 58}{space 4}-.0595347{col 71}{space 3}   .10043
{txt}{space 16} {c |}
{space 9}marital {c |}
{space 8}Married  {c |}{col 18}{res}{space 2}-.1335605{col 30}{space 2} .3302771{col 41}{space 1}   -0.40{col 50}{space 3}0.686{col 58}{space 4}-.7808916{col 71}{space 3} .5137707
{txt}{space 7}evaluate1 {c |}{col 18}{res}{space 2} .0786852{col 30}{space 2}   .33888{col 41}{space 1}    0.23{col 50}{space 3}0.816{col 58}{space 4}-.5855073{col 71}{space 3} .7428778
{txt}{space 12}nfc1 {c |}{col 18}{res}{space 2}-.2151531{col 30}{space 2} .2039835{col 41}{space 1}   -1.05{col 50}{space 3}0.292{col 58}{space 4}-.6149534{col 71}{space 3} .1846472
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
           /cut1 {c |}{col 18}{res}{space 2}-1.542167{col 30}{space 2}  1.07916{col 58}{space 4}-3.657281{col 71}{space 3} .5729478
{txt}           /cut2 {c |}{col 18}{res}{space 2} .0824502{col 30}{space 2} 1.044484{col 58}{space 4}  -1.9647{col 71}{space 3}   2.1296
{txt}           /cut3 {c |}{col 18}{res}{space 2} 1.629203{col 30}{space 2} 1.032646{col 58}{space 4}-.3947459{col 71}{space 3} 3.653152
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -730.5074}  
Iteration 1:{space 3}log pseudolikelihood = {res:-663.05883}  
Iteration 2:{space 3}log pseudolikelihood = {res:-661.78297}  
Iteration 3:{space 3}log pseudolikelihood = {res:-661.77955}  
Iteration 4:{space 3}log pseudolikelihood = {res:-661.77955}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       620
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     58.49
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-661.77955{txt}{col 51}Pseudo R2{col 67}= {res}    0.0941

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}    pid_str19_full{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      z{col 52}   P>|z|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
disagree_total_int {c |}{col 20}{res}{space 2}-.1026116{col 32}{space 2} .0175135{col 43}{space 1}   -5.86{col 52}{space 3}0.000{col 60}{space 4}-.1369374{col 73}{space 3}-.0682859
{txt}{space 9}numgiven1 {c |}{col 20}{res}{space 2}-.3516994{col 32}{space 2} .2088124{col 43}{space 1}   -1.68{col 52}{space 3}0.092{col 60}{space 4}-.7609642{col 73}{space 3} .0575654
{txt}{space 2}network_interest {c |}{col 20}{res}{space 2}-.1904289{col 32}{space 2} .1722145{col 43}{space 1}   -1.11{col 52}{space 3}0.269{col 60}{space 4}-.5279631{col 73}{space 3} .1471053
{txt}{space 18} {c |}
{space 12}pid_21 {c |}
{space 9}Democrat  {c |}{col 20}{res}{space 2} .4239326{col 32}{space 2} .2466679{col 43}{space 1}    1.72{col 52}{space 3}0.086{col 60}{space 4}-.0595276{col 73}{space 3} .9073928
{txt}{space 7}interest_w1 {c |}{col 20}{res}{space 2} .3552405{col 32}{space 2} .1605084{col 43}{space 1}    2.21{col 52}{space 3}0.027{col 60}{space 4} .0406498{col 73}{space 3} .6698312
{txt}{space 18} {c |}
{space 12}gender {c |}
{space 11}Female  {c |}{col 20}{res}{space 2} .5368534{col 32}{space 2} .2498912{col 43}{space 1}    2.15{col 52}{space 3}0.032{col 60}{space 4} .0470757{col 73}{space 3} 1.026631
{txt}{space 18} {c |}
{space 10}race_eth {c |}
{space 12}Black  {c |}{col 20}{res}{space 2} 1.118397{col 32}{space 2} .7052181{col 43}{space 1}    1.59{col 52}{space 3}0.113{col 60}{space 4}-.2638052{col 73}{space 3} 2.500599
{txt}{space 9}Hispanic  {c |}{col 20}{res}{space 2}-.0838484{col 32}{space 2} .5110103{col 43}{space 1}   -0.16{col 52}{space 3}0.870{col 60}{space 4} -1.08541{col 73}{space 3} .9177133
{txt}{space 12}Other  {c |}{col 20}{res}{space 2} .3615551{col 32}{space 2} .7432494{col 43}{space 1}    0.49{col 52}{space 3}0.627{col 60}{space 4}-1.095187{col 73}{space 3} 1.818297
{txt}{space 18} {c |}
{space 15}age {c |}{col 20}{res}{space 2} .0048783{col 32}{space 2} .0091979{col 43}{space 1}    0.53{col 52}{space 3}0.596{col 60}{space 4}-.0131492{col 73}{space 3} .0229058
{txt}{space 14}educ {c |}{col 20}{res}{space 2} .0259459{col 32}{space 2} .1431911{col 43}{space 1}    0.18{col 52}{space 3}0.856{col 60}{space 4}-.2547035{col 73}{space 3} .3065954
{txt}{space 12}income {c |}{col 20}{res}{space 2} .0250845{col 32}{space 2} .0416732{col 43}{space 1}    0.60{col 52}{space 3}0.547{col 60}{space 4}-.0565935{col 73}{space 3} .1067624
{txt}{space 18} {c |}
{space 11}marital {c |}
{space 10}Married  {c |}{col 20}{res}{space 2}-.1264953{col 32}{space 2}  .331892{col 43}{space 1}   -0.38{col 52}{space 3}0.703{col 60}{space 4}-.7769917{col 73}{space 3} .5240012
{txt}{space 9}evaluate1 {c |}{col 20}{res}{space 2}  .057158{col 32}{space 2} .3373651{col 43}{space 1}    0.17{col 52}{space 3}0.865{col 60}{space 4}-.6040655{col 73}{space 3} .7183814
{txt}{space 14}nfc1 {c |}{col 20}{res}{space 2}-.1964089{col 32}{space 2} .2067218{col 43}{space 1}   -0.95{col 52}{space 3}0.342{col 60}{space 4}-.6015763{col 73}{space 3} .2087585
{txt}{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
             /cut1 {c |}{col 20}{res}{space 2}-1.724827{col 32}{space 2} 1.091897{col 60}{space 4}-3.864906{col 73}{space 3} .4152528
{txt}             /cut2 {c |}{col 20}{res}{space 2}-.0874319{col 32}{space 2} 1.043859{col 60}{space 4}-2.133358{col 73}{space 3} 1.958494
{txt}             /cut3 {c |}{col 20}{res}{space 2} 1.463327{col 32}{space 2}  1.03669{col 60}{space 4} -.568547{col 73}{space 3} 3.495202
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -730.5074}  
Iteration 1:{space 3}log pseudolikelihood = {res:-659.55401}  
Iteration 2:{space 3}log pseudolikelihood = {res:-658.22149}  
Iteration 3:{space 3}log pseudolikelihood = {res:-658.21781}  
Iteration 4:{space 3}log pseudolikelihood = {res:-658.21781}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       620
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     69.79
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-658.21781{txt}{col 51}Pseudo R2{col 67}= {res}    0.0990

{txt}{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}      pid_str19_full{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
disagree_total_close {c |}{col 22}{res}{space 2}-.0991941{col 34}{space 2} .0153884{col 45}{space 1}   -6.45{col 54}{space 3}0.000{col 62}{space 4}-.1293549{col 75}{space 3}-.0690333
{txt}{space 11}numgiven1 {c |}{col 22}{res}{space 2} -.317846{col 34}{space 2} .2034998{col 45}{space 1}   -1.56{col 54}{space 3}0.118{col 62}{space 4}-.7166982{col 75}{space 3} .0810062
{txt}{space 4}network_interest {c |}{col 22}{res}{space 2}-.0887312{col 34}{space 2} .1755966{col 45}{space 1}   -0.51{col 54}{space 3}0.613{col 62}{space 4}-.4328941{col 75}{space 3} .2554317
{txt}{space 20} {c |}
{space 14}pid_21 {c |}
{space 11}Democrat  {c |}{col 22}{res}{space 2} .4325343{col 34}{space 2} .2512371{col 45}{space 1}    1.72{col 54}{space 3}0.085{col 62}{space 4}-.0598813{col 75}{space 3} .9249499
{txt}{space 9}interest_w1 {c |}{col 22}{res}{space 2} .3800671{col 34}{space 2} .1563402{col 45}{space 1}    2.43{col 54}{space 3}0.015{col 62}{space 4} .0736458{col 75}{space 3} .6864883
{txt}{space 20} {c |}
{space 14}gender {c |}
{space 13}Female  {c |}{col 22}{res}{space 2} .5220633{col 34}{space 2} .2449219{col 45}{space 1}    2.13{col 54}{space 3}0.033{col 62}{space 4} .0420252{col 75}{space 3} 1.002101
{txt}{space 20} {c |}
{space 12}race_eth {c |}
{space 14}Black  {c |}{col 22}{res}{space 2} 1.141821{col 34}{space 2} .6871828{col 45}{space 1}    1.66{col 54}{space 3}0.097{col 62}{space 4}-.2050327{col 75}{space 3} 2.488675
{txt}{space 11}Hispanic  {c |}{col 22}{res}{space 2} .0462783{col 34}{space 2} .5052342{col 45}{space 1}    0.09{col 54}{space 3}0.927{col 62}{space 4}-.9439625{col 75}{space 3} 1.036519
{txt}{space 14}Other  {c |}{col 22}{res}{space 2} .5060839{col 34}{space 2} .8204413{col 45}{space 1}    0.62{col 54}{space 3}0.537{col 62}{space 4}-1.101951{col 75}{space 3} 2.114119
{txt}{space 20} {c |}
{space 17}age {c |}{col 22}{res}{space 2} .0038386{col 34}{space 2} .0090283{col 45}{space 1}    0.43{col 54}{space 3}0.671{col 62}{space 4}-.0138565{col 75}{space 3} .0215336
{txt}{space 16}educ {c |}{col 22}{res}{space 2} .0141885{col 34}{space 2} .1391799{col 45}{space 1}    0.10{col 54}{space 3}0.919{col 62}{space 4}-.2585991{col 75}{space 3} .2869761
{txt}{space 14}income {c |}{col 22}{res}{space 2}  .027836{col 34}{space 2} .0399218{col 45}{space 1}    0.70{col 54}{space 3}0.486{col 62}{space 4}-.0504094{col 75}{space 3} .1060813
{txt}{space 20} {c |}
{space 13}marital {c |}
{space 12}Married  {c |}{col 22}{res}{space 2}-.1608059{col 34}{space 2} .3258233{col 45}{space 1}   -0.49{col 54}{space 3}0.622{col 62}{space 4}-.7994079{col 75}{space 3}  .477796
{txt}{space 11}evaluate1 {c |}{col 22}{res}{space 2} .0320676{col 34}{space 2} .3500435{col 45}{space 1}    0.09{col 54}{space 3}0.927{col 62}{space 4}-.6540051{col 75}{space 3} .7181403
{txt}{space 16}nfc1 {c |}{col 22}{res}{space 2}-.2103715{col 34}{space 2} .2035343{col 45}{space 1}   -1.03{col 54}{space 3}0.301{col 62}{space 4}-.6092914{col 75}{space 3} .1885484
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
               /cut1 {c |}{col 22}{res}{space 2}-1.275309{col 34}{space 2} 1.044791{col 62}{space 4}-3.323061{col 75}{space 3} .7724425
{txt}               /cut2 {c |}{col 22}{res}{space 2} .3744537{col 34}{space 2} 1.004187{col 62}{space 4}-1.593717{col 75}{space 3} 2.342624
{txt}               /cut3 {c |}{col 22}{res}{space 2} 1.940346{col 34}{space 2} .9969377{col 62}{space 4} -.013616{col 75}{space 3} 3.894308
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -730.5074}  
Iteration 1:{space 3}log pseudolikelihood = {res:-660.48446}  
Iteration 2:{space 3}log pseudolikelihood = {res:-659.26194}  
Iteration 3:{space 3}log pseudolikelihood = {res:-659.25852}  
Iteration 4:{space 3}log pseudolikelihood = {res:-659.25852}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       620
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     70.14
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-659.25852{txt}{col 51}Pseudo R2{col 67}= {res}    0.0975

{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}    Robust
{col 1}       pid_str19_full{col 23}{c |}      Coef.{col 35}   Std. Err.{col 47}      z{col 55}   P>|z|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
disagree_total_weight {c |}{col 23}{res}{space 2}-.1109093{col 35}{space 2} .0184404{col 46}{space 1}   -6.01{col 55}{space 3}0.000{col 63}{space 4}-.1470519{col 76}{space 3}-.0747667
{txt}{space 12}numgiven1 {c |}{col 23}{res}{space 2}-.3843226{col 35}{space 2} .2037723{col 46}{space 1}   -1.89{col 55}{space 3}0.059{col 63}{space 4}-.7837089{col 76}{space 3} .0150638
{txt}{space 5}network_interest {c |}{col 23}{res}{space 2}-.1051392{col 35}{space 2} .1760451{col 46}{space 1}   -0.60{col 55}{space 3}0.550{col 63}{space 4}-.4501813{col 76}{space 3}  .239903
{txt}{space 21} {c |}
{space 15}pid_21 {c |}
{space 12}Democrat  {c |}{col 23}{res}{space 2} .4640946{col 35}{space 2}   .24936{col 46}{space 1}    1.86{col 55}{space 3}0.063{col 63}{space 4}-.0246421{col 76}{space 3} .9528314
{txt}{space 10}interest_w1 {c |}{col 23}{res}{space 2} .3941915{col 35}{space 2} .1558069{col 46}{space 1}    2.53{col 55}{space 3}0.011{col 63}{space 4} .0888156{col 76}{space 3} .6995674
{txt}{space 21} {c |}
{space 15}gender {c |}
{space 14}Female  {c |}{col 23}{res}{space 2} .5520173{col 35}{space 2} .2480665{col 46}{space 1}    2.23{col 55}{space 3}0.026{col 63}{space 4} .0658159{col 76}{space 3} 1.038219
{txt}{space 21} {c |}
{space 13}race_eth {c |}
{space 15}Black  {c |}{col 23}{res}{space 2} 1.178298{col 35}{space 2}  .652773{col 46}{space 1}    1.81{col 55}{space 3}0.071{col 63}{space 4}-.1011136{col 76}{space 3}  2.45771
{txt}{space 12}Hispanic  {c |}{col 23}{res}{space 2}-.0107718{col 35}{space 2} .5029242{col 46}{space 1}   -0.02{col 55}{space 3}0.983{col 63}{space 4}-.9964851{col 76}{space 3} .9749414
{txt}{space 15}Other  {c |}{col 23}{res}{space 2} .2890573{col 35}{space 2} .6879088{col 46}{space 1}    0.42{col 55}{space 3}0.674{col 63}{space 4}-1.059219{col 76}{space 3} 1.637334
{txt}{space 21} {c |}
{space 18}age {c |}{col 23}{res}{space 2} .0025156{col 35}{space 2} .0091841{col 46}{space 1}    0.27{col 55}{space 3}0.784{col 63}{space 4}-.0154849{col 76}{space 3} .0205162
{txt}{space 17}educ {c |}{col 23}{res}{space 2} .0454855{col 35}{space 2} .1385197{col 46}{space 1}    0.33{col 55}{space 3}0.743{col 63}{space 4}-.2260081{col 76}{space 3} .3169791
{txt}{space 15}income {c |}{col 23}{res}{space 2} .0232535{col 35}{space 2} .0402988{col 46}{space 1}    0.58{col 55}{space 3}0.564{col 63}{space 4}-.0557306{col 76}{space 3} .1022376
{txt}{space 21} {c |}
{space 14}marital {c |}
{space 13}Married  {c |}{col 23}{res}{space 2}-.1036945{col 35}{space 2} .3268142{col 46}{space 1}   -0.32{col 55}{space 3}0.751{col 63}{space 4}-.7442385{col 76}{space 3} .5368495
{txt}{space 12}evaluate1 {c |}{col 23}{res}{space 2} .0120814{col 35}{space 2} .3352972{col 46}{space 1}    0.04{col 55}{space 3}0.971{col 63}{space 4}-.6450891{col 76}{space 3} .6692519
{txt}{space 17}nfc1 {c |}{col 23}{res}{space 2}-.2050262{col 35}{space 2} .2060065{col 46}{space 1}   -1.00{col 55}{space 3}0.320{col 63}{space 4}-.6087914{col 76}{space 3} .1987391
{txt}{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                /cut1 {c |}{col 23}{res}{space 2}-1.289165{col 35}{space 2} 1.055897{col 63}{space 4}-3.358685{col 76}{space 3} .7803557
{txt}                /cut2 {c |}{col 23}{res}{space 2}  .342066{col 35}{space 2}  1.01242{col 63}{space 4} -1.64224{col 76}{space 3} 2.326372
{txt}                /cut3 {c |}{col 23}{res}{space 2}  1.90429{col 35}{space 2} 1.005183{col 63}{space 4}-.0658334{col 76}{space 3} 3.874413
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est5{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-731.14259}  
Iteration 1:{space 3}log pseudolikelihood = {res: -676.8129}  
Iteration 2:{space 3}log pseudolikelihood = {res:-676.38689}  
Iteration 3:{space 3}log pseudolikelihood = {res:-676.38664}  
Iteration 4:{space 3}log pseudolikelihood = {res:-676.38664}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       621
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     59.58
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-676.38664{txt}{col 51}Pseudo R2{col 67}= {res}    0.0749

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}  pid_str19_full{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}gendiff {c |}{col 18}{res}{space 2}-.6320295{col 30}{space 2}  .151039{col 41}{space 1}   -4.18{col 50}{space 3}0.000{col 58}{space 4}-.9280605{col 71}{space 3}-.3359985
{txt}{space 7}numgiven1 {c |}{col 18}{res}{space 2}-.0376931{col 30}{space 2} .1937983{col 41}{space 1}   -0.19{col 50}{space 3}0.846{col 58}{space 4}-.4175309{col 71}{space 3} .3421446
{txt}network_interest {c |}{col 18}{res}{space 2}-.0906083{col 30}{space 2} .1715093{col 41}{space 1}   -0.53{col 50}{space 3}0.597{col 58}{space 4}-.4267604{col 71}{space 3} .2455438
{txt}{space 16} {c |}
{space 10}pid_21 {c |}
{space 7}Democrat  {c |}{col 18}{res}{space 2} .4564554{col 30}{space 2} .2507723{col 41}{space 1}    1.82{col 50}{space 3}0.069{col 58}{space 4}-.0350493{col 71}{space 3}   .94796
{txt}{space 5}interest_w1 {c |}{col 18}{res}{space 2} .3893548{col 30}{space 2} .1529613{col 41}{space 1}    2.55{col 50}{space 3}0.011{col 58}{space 4} .0895562{col 71}{space 3} .6891534
{txt}{space 16} {c |}
{space 10}gender {c |}
{space 9}Female  {c |}{col 18}{res}{space 2} .7181822{col 30}{space 2} .2432277{col 41}{space 1}    2.95{col 50}{space 3}0.003{col 58}{space 4} .2414647{col 71}{space 3}   1.1949
{txt}{space 16} {c |}
{space 8}race_eth {c |}
{space 10}Black  {c |}{col 18}{res}{space 2} 1.383726{col 30}{space 2} .5093711{col 41}{space 1}    2.72{col 50}{space 3}0.007{col 58}{space 4}  .385377{col 71}{space 3} 2.382075
{txt}{space 7}Hispanic  {c |}{col 18}{res}{space 2}-.1838865{col 30}{space 2} .5703998{col 41}{space 1}   -0.32{col 50}{space 3}0.747{col 58}{space 4} -1.30185{col 71}{space 3} .9340766
{txt}{space 10}Other  {c |}{col 18}{res}{space 2}-.4262858{col 30}{space 2} .6450278{col 41}{space 1}   -0.66{col 50}{space 3}0.509{col 58}{space 4}-1.690517{col 71}{space 3} .8379455
{txt}{space 16} {c |}
{space 13}age {c |}{col 18}{res}{space 2}  .000712{col 30}{space 2} .0089611{col 41}{space 1}    0.08{col 50}{space 3}0.937{col 58}{space 4}-.0168514{col 71}{space 3} .0182754
{txt}{space 12}educ {c |}{col 18}{res}{space 2} .0917266{col 30}{space 2} .1408119{col 41}{space 1}    0.65{col 50}{space 3}0.515{col 58}{space 4}-.1842597{col 71}{space 3} .3677129
{txt}{space 10}income {c |}{col 18}{res}{space 2} .0140409{col 30}{space 2} .0384292{col 41}{space 1}    0.37{col 50}{space 3}0.715{col 58}{space 4} -.061279{col 71}{space 3} .0893608
{txt}{space 16} {c |}
{space 9}marital {c |}
{space 8}Married  {c |}{col 18}{res}{space 2} .0667429{col 30}{space 2} .3154067{col 41}{space 1}    0.21{col 50}{space 3}0.832{col 58}{space 4}-.5514429{col 71}{space 3} .6849287
{txt}{space 7}evaluate1 {c |}{col 18}{res}{space 2} .1289631{col 30}{space 2} .3342586{col 41}{space 1}    0.39{col 50}{space 3}0.700{col 58}{space 4}-.5261717{col 71}{space 3} .7840979
{txt}{space 12}nfc1 {c |}{col 18}{res}{space 2}-.2037818{col 30}{space 2} .2035323{col 41}{space 1}   -1.00{col 50}{space 3}0.317{col 58}{space 4}-.6026978{col 71}{space 3} .1951341
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
           /cut1 {c |}{col 18}{res}{space 2}-1.901302{col 30}{space 2} .9767024{col 58}{space 4}-3.815603{col 71}{space 3} .0129996
{txt}           /cut2 {c |}{col 18}{res}{space 2}-.3421267{col 30}{space 2}  .928659{col 58}{space 4}-2.162265{col 71}{space 3} 1.478012
{txt}           /cut3 {c |}{col 18}{res}{space 2} 1.143947{col 30}{space 2} .9276324{col 58}{space 4}-.6741789{col 71}{space 3} 2.962073
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est6{txt} stored)

{com}. 
. esttab using 2008JULY_ALTMEASURES_PARTISANSHIP.rtf, onecell nobaselevels replace label pr2 aic bic se star(+ 0.10 * 0.05 ** 0.01) ///
>         mtitles("Original Measure" "(/D+A)" "Weigh: by Disc. Interest" ///
>                 "Weigh: by Tie Strength" "Weighted by Gen Dis" "Gen Disagree")  ///
>         title({c -(}\b Table XX.{c )-} "Alternative Measures of Disagreement - 2008-2009 ANES (July 2009)") ///
>         rename(disagree_total Disagreement disagree_avg "Disagreement" disagree_total_int Disagreement disagree_total_close Disagreement ///
>                                 disagree_total_weight Disagreement gendiff Disagreement) 
{res}{txt}(output written to {browse  `"2008JULY_ALTMEASURES_PARTISANSHIP.rtf"'})

{com}. 
. eststo clear
{txt}
{com}. 
.         
.         
. /***Partisan Ambivalence****/
. *in likes
. eststo clear
{txt}
{com}. foreach var in disagree_total disagree_avg disagree_total_int disagree_total_close  ///
>         disagree_total_weight gendiff  {c -(}
{txt}  2{com}.         eststo: ologit in_like_w11  `var'  numgiven1 network_interest  i.pid_21 interest_w1 i.gender ///
>                         i.race age educ income i.marital evaluate1 nfc1 [pweight= WGTC11]
{txt}  3{com}.         {c )-}               

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1044.0756}  
Iteration 1:{space 3}log pseudolikelihood = {res:-983.64265}  
Iteration 2:{space 3}log pseudolikelihood = {res:-982.34164}  
Iteration 3:{space 3}log pseudolikelihood = {res:-982.33985}  
Iteration 4:{space 3}log pseudolikelihood = {res:-982.33985}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       757
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     63.24
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-982.33985{txt}{col 51}Pseudo R2{col 67}= {res}    0.0591

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}     in_like_w11{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}disagree_total {c |}{col 18}{res}{space 2} -.107698{col 30}{space 2} .0577892{col 41}{space 1}   -1.86{col 50}{space 3}0.062{col 58}{space 4}-.2209627{col 71}{space 3} .0055667
{txt}{space 7}numgiven1 {c |}{col 18}{res}{space 2}-.0232796{col 30}{space 2} .2829243{col 41}{space 1}   -0.08{col 50}{space 3}0.934{col 58}{space 4}-.5778011{col 71}{space 3} .5312418
{txt}network_interest {c |}{col 18}{res}{space 2} .1504478{col 30}{space 2} .1272391{col 41}{space 1}    1.18{col 50}{space 3}0.237{col 58}{space 4}-.0989362{col 71}{space 3} .3998318
{txt}{space 16} {c |}
{space 10}pid_21 {c |}
{space 7}Democrat  {c |}{col 18}{res}{space 2} .5887379{col 30}{space 2} .2166889{col 41}{space 1}    2.72{col 50}{space 3}0.007{col 58}{space 4} .1640355{col 71}{space 3}  1.01344
{txt}{space 5}interest_w1 {c |}{col 18}{res}{space 2} .4890772{col 30}{space 2} .1369148{col 41}{space 1}    3.57{col 50}{space 3}0.000{col 58}{space 4} .2207291{col 71}{space 3} .7574253
{txt}{space 16} {c |}
{space 10}gender {c |}
{space 9}Female  {c |}{col 18}{res}{space 2} .4566956{col 30}{space 2}  .227801{col 41}{space 1}    2.00{col 50}{space 3}0.045{col 58}{space 4}  .010214{col 71}{space 3} .9031773
{txt}{space 16} {c |}
{space 8}race_eth {c |}
{space 10}Black  {c |}{col 18}{res}{space 2} .5029049{col 30}{space 2} .4233232{col 41}{space 1}    1.19{col 50}{space 3}0.235{col 58}{space 4}-.3267933{col 71}{space 3} 1.332603
{txt}{space 7}Hispanic  {c |}{col 18}{res}{space 2}-.6274982{col 30}{space 2} .4641473{col 41}{space 1}   -1.35{col 50}{space 3}0.176{col 58}{space 4} -1.53721{col 71}{space 3} .2822139
{txt}{space 10}Other  {c |}{col 18}{res}{space 2}-.2079796{col 30}{space 2} 1.262719{col 41}{space 1}   -0.16{col 50}{space 3}0.869{col 58}{space 4}-2.682863{col 71}{space 3} 2.266904
{txt}{space 16} {c |}
{space 13}age {c |}{col 18}{res}{space 2}-.0148543{col 30}{space 2} .0074721{col 41}{space 1}   -1.99{col 50}{space 3}0.047{col 58}{space 4}-.0294993{col 71}{space 3}-.0002094
{txt}{space 12}educ {c |}{col 18}{res}{space 2} .0208482{col 30}{space 2} .1147136{col 41}{space 1}    0.18{col 50}{space 3}0.856{col 58}{space 4}-.2039862{col 71}{space 3} .2456827
{txt}{space 10}income {c |}{col 18}{res}{space 2} .0008968{col 30}{space 2} .0304134{col 41}{space 1}    0.03{col 50}{space 3}0.976{col 58}{space 4}-.0587124{col 71}{space 3}  .060506
{txt}{space 16} {c |}
{space 9}marital {c |}
{space 8}Married  {c |}{col 18}{res}{space 2} .2078715{col 30}{space 2} .2987858{col 41}{space 1}    0.70{col 50}{space 3}0.487{col 58}{space 4}-.3777379{col 71}{space 3} .7934808
{txt}{space 7}evaluate1 {c |}{col 18}{res}{space 2} .2920715{col 30}{space 2} .3096724{col 41}{space 1}    0.94{col 50}{space 3}0.346{col 58}{space 4}-.3148751{col 71}{space 3} .8990182
{txt}{space 12}nfc1 {c |}{col 18}{res}{space 2}-.1199179{col 30}{space 2} .1705834{col 41}{space 1}   -0.70{col 50}{space 3}0.482{col 58}{space 4}-.4542552{col 71}{space 3} .2144193
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
           /cut1 {c |}{col 18}{res}{space 2} .8956677{col 30}{space 2} 1.164295{col 58}{space 4}-1.386308{col 71}{space 3} 3.177643
{txt}           /cut2 {c |}{col 18}{res}{space 2} 1.336517{col 30}{space 2} 1.163988{col 58}{space 4}-.9448574{col 71}{space 3} 3.617892
{txt}           /cut3 {c |}{col 18}{res}{space 2} 3.043616{col 30}{space 2} 1.182692{col 58}{space 4} .7255825{col 71}{space 3} 5.361649
{txt}           /cut4 {c |}{col 18}{res}{space 2}  4.76485{col 30}{space 2}  1.24261{col 58}{space 4}  2.32938{col 71}{space 3} 7.200321
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1044.0756}  
Iteration 1:{space 3}log pseudolikelihood = {res:-979.56612}  
Iteration 2:{space 3}log pseudolikelihood = {res: -978.0973}  
Iteration 3:{space 3}log pseudolikelihood = {res:-978.09508}  
Iteration 4:{space 3}log pseudolikelihood = {res:-978.09508}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       757
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     68.63
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-978.09508{txt}{col 51}Pseudo R2{col 67}= {res}    0.0632

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}     in_like_w11{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}disagree_avg {c |}{col 18}{res}{space 2}-.4258991{col 30}{space 2} .1676332{col 41}{space 1}   -2.54{col 50}{space 3}0.011{col 58}{space 4}-.7544541{col 71}{space 3} -.097344
{txt}{space 7}numgiven1 {c |}{col 18}{res}{space 2}-.0361433{col 30}{space 2} .2704031{col 41}{space 1}   -0.13{col 50}{space 3}0.894{col 58}{space 4}-.5661235{col 71}{space 3}  .493837
{txt}network_interest {c |}{col 18}{res}{space 2} .1422802{col 30}{space 2} .1257787{col 41}{space 1}    1.13{col 50}{space 3}0.258{col 58}{space 4}-.1042416{col 71}{space 3} .3888019
{txt}{space 16} {c |}
{space 10}pid_21 {c |}
{space 7}Democrat  {c |}{col 18}{res}{space 2}    .5937{col 30}{space 2} .2180302{col 41}{space 1}    2.72{col 50}{space 3}0.006{col 58}{space 4} .1663686{col 71}{space 3} 1.021031
{txt}{space 5}interest_w1 {c |}{col 18}{res}{space 2} .4967784{col 30}{space 2} .1370397{col 41}{space 1}    3.63{col 50}{space 3}0.000{col 58}{space 4} .2281856{col 71}{space 3} .7653713
{txt}{space 16} {c |}
{space 10}gender {c |}
{space 9}Female  {c |}{col 18}{res}{space 2} .4293301{col 30}{space 2}  .228505{col 41}{space 1}    1.88{col 50}{space 3}0.060{col 58}{space 4}-.0185314{col 71}{space 3} .8771916
{txt}{space 16} {c |}
{space 8}race_eth {c |}
{space 10}Black  {c |}{col 18}{res}{space 2} .4928594{col 30}{space 2} .4235949{col 41}{space 1}    1.16{col 50}{space 3}0.245{col 58}{space 4}-.3373715{col 71}{space 3}  1.32309
{txt}{space 7}Hispanic  {c |}{col 18}{res}{space 2}-.6061688{col 30}{space 2} .4442985{col 41}{space 1}   -1.36{col 50}{space 3}0.172{col 58}{space 4}-1.476978{col 71}{space 3} .2646403
{txt}{space 10}Other  {c |}{col 18}{res}{space 2}-.1921247{col 30}{space 2} 1.307937{col 41}{space 1}   -0.15{col 50}{space 3}0.883{col 58}{space 4}-2.755634{col 71}{space 3} 2.371385
{txt}{space 16} {c |}
{space 13}age {c |}{col 18}{res}{space 2} -.014429{col 30}{space 2} .0074906{col 41}{space 1}   -1.93{col 50}{space 3}0.054{col 58}{space 4}-.0291103{col 71}{space 3} .0002522
{txt}{space 12}educ {c |}{col 18}{res}{space 2} .0134421{col 30}{space 2} .1156134{col 41}{space 1}    0.12{col 50}{space 3}0.907{col 58}{space 4}-.2131559{col 71}{space 3} .2400401
{txt}{space 10}income {c |}{col 18}{res}{space 2} .0018339{col 30}{space 2} .0304053{col 41}{space 1}    0.06{col 50}{space 3}0.952{col 58}{space 4}-.0577594{col 71}{space 3} .0614272
{txt}{space 16} {c |}
{space 9}marital {c |}
{space 8}Married  {c |}{col 18}{res}{space 2} .2100067{col 30}{space 2} .2982611{col 41}{space 1}    0.70{col 50}{space 3}0.481{col 58}{space 4}-.3745743{col 71}{space 3} .7945876
{txt}{space 7}evaluate1 {c |}{col 18}{res}{space 2} .2779002{col 30}{space 2} .3118574{col 41}{space 1}    0.89{col 50}{space 3}0.373{col 58}{space 4} -.333329{col 71}{space 3} .8891294
{txt}{space 12}nfc1 {c |}{col 18}{res}{space 2}-.1156169{col 30}{space 2} .1700421{col 41}{space 1}   -0.68{col 50}{space 3}0.497{col 58}{space 4}-.4488933{col 71}{space 3} .2176594
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
           /cut1 {c |}{col 18}{res}{space 2} .8735356{col 30}{space 2} 1.118781{col 58}{space 4}-1.319234{col 71}{space 3} 3.066306
{txt}           /cut2 {c |}{col 18}{res}{space 2} 1.317879{col 30}{space 2} 1.118288{col 58}{space 4}-.8739245{col 71}{space 3} 3.509682
{txt}           /cut3 {c |}{col 18}{res}{space 2} 3.037986{col 30}{space 2} 1.134587{col 58}{space 4} .8142374{col 71}{space 3} 5.261736
{txt}           /cut4 {c |}{col 18}{res}{space 2} 4.770673{col 30}{space 2} 1.191983{col 58}{space 4} 2.434428{col 71}{space 3} 7.106917
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1044.0756}  
Iteration 1:{space 3}log pseudolikelihood = {res:-982.73218}  
Iteration 2:{space 3}log pseudolikelihood = {res:-981.39192}  
Iteration 3:{space 3}log pseudolikelihood = {res:-981.38999}  
Iteration 4:{space 3}log pseudolikelihood = {res:-981.38999}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       757
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     65.27
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-981.38999{txt}{col 51}Pseudo R2{col 67}= {res}    0.0600

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}       in_like_w11{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      z{col 52}   P>|z|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
disagree_total_int {c |}{col 20}{res}{space 2}-.0311022{col 32}{space 2} .0141289{col 43}{space 1}   -2.20{col 52}{space 3}0.028{col 60}{space 4}-.0587944{col 73}{space 3}  -.00341
{txt}{space 9}numgiven1 {c |}{col 20}{res}{space 2}-.0287331{col 32}{space 2} .2831928{col 43}{space 1}   -0.10{col 52}{space 3}0.919{col 60}{space 4}-.5837809{col 73}{space 3} .5263147
{txt}{space 2}network_interest {c |}{col 20}{res}{space 2} .1204257{col 32}{space 2} .1279785{col 43}{space 1}    0.94{col 52}{space 3}0.347{col 60}{space 4}-.1304075{col 73}{space 3}  .371259
{txt}{space 18} {c |}
{space 12}pid_21 {c |}
{space 9}Democrat  {c |}{col 20}{res}{space 2} .5827332{col 32}{space 2} .2158564{col 43}{space 1}    2.70{col 52}{space 3}0.007{col 60}{space 4} .1596623{col 73}{space 3} 1.005804
{txt}{space 7}interest_w1 {c |}{col 20}{res}{space 2} .4843466{col 32}{space 2} .1376093{col 43}{space 1}    3.52{col 52}{space 3}0.000{col 60}{space 4} .2146374{col 73}{space 3} .7540558
{txt}{space 18} {c |}
{space 12}gender {c |}
{space 11}Female  {c |}{col 20}{res}{space 2} .4566997{col 32}{space 2} .2273464{col 43}{space 1}    2.01{col 52}{space 3}0.045{col 60}{space 4} .0111088{col 73}{space 3} .9022905
{txt}{space 18} {c |}
{space 10}race_eth {c |}
{space 12}Black  {c |}{col 20}{res}{space 2} .4992619{col 32}{space 2} .4268861{col 43}{space 1}    1.17{col 52}{space 3}0.242{col 60}{space 4}-.3374195{col 73}{space 3} 1.335943
{txt}{space 9}Hispanic  {c |}{col 20}{res}{space 2}-.6268859{col 32}{space 2} .4645413{col 43}{space 1}   -1.35{col 52}{space 3}0.177{col 60}{space 4} -1.53737{col 73}{space 3} .2835983
{txt}{space 12}Other  {c |}{col 20}{res}{space 2}-.2203605{col 32}{space 2} 1.260311{col 43}{space 1}   -0.17{col 52}{space 3}0.861{col 60}{space 4}-2.690524{col 73}{space 3} 2.249803
{txt}{space 18} {c |}
{space 15}age {c |}{col 20}{res}{space 2}-.0144321{col 32}{space 2} .0074827{col 43}{space 1}   -1.93{col 52}{space 3}0.054{col 60}{space 4} -.029098{col 73}{space 3} .0002338
{txt}{space 14}educ {c |}{col 20}{res}{space 2} .0162767{col 32}{space 2} .1156121{col 43}{space 1}    0.14{col 52}{space 3}0.888{col 60}{space 4}-.2103189{col 73}{space 3} .2428723
{txt}{space 12}income {c |}{col 20}{res}{space 2}  .002622{col 32}{space 2} .0307334{col 43}{space 1}    0.09{col 52}{space 3}0.932{col 60}{space 4}-.0576144{col 73}{space 3} .0628584
{txt}{space 18} {c |}
{space 11}marital {c |}
{space 10}Married  {c |}{col 20}{res}{space 2} .2148811{col 32}{space 2} .2985954{col 43}{space 1}    0.72{col 52}{space 3}0.472{col 60}{space 4}-.3703552{col 73}{space 3} .8001174
{txt}{space 9}evaluate1 {c |}{col 20}{res}{space 2}  .287626{col 32}{space 2} .3104737{col 43}{space 1}    0.93{col 52}{space 3}0.354{col 60}{space 4}-.3208912{col 73}{space 3} .8961433
{txt}{space 14}nfc1 {c |}{col 20}{res}{space 2}-.1132229{col 32}{space 2} .1710177{col 43}{space 1}   -0.66{col 52}{space 3}0.508{col 60}{space 4}-.4484115{col 73}{space 3} .2219656
{txt}{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
             /cut1 {c |}{col 20}{res}{space 2} .7966207{col 32}{space 2} 1.178304{col 60}{space 4}-1.512812{col 73}{space 3} 3.106053
{txt}             /cut2 {c |}{col 20}{res}{space 2}  1.23703{col 32}{space 2} 1.177364{col 60}{space 4}-1.070561{col 73}{space 3}  3.54462
{txt}             /cut3 {c |}{col 20}{res}{space 2} 2.946756{col 32}{space 2} 1.195446{col 60}{space 4} .6037247{col 73}{space 3} 5.289787
{txt}             /cut4 {c |}{col 20}{res}{space 2} 4.673687{col 32}{space 2} 1.255157{col 60}{space 4} 2.213626{col 73}{space 3} 7.133749
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1044.0756}  
Iteration 1:{space 3}log pseudolikelihood = {res:-982.96246}  
Iteration 2:{space 3}log pseudolikelihood = {res:-981.67007}  
Iteration 3:{space 3}log pseudolikelihood = {res:-981.66821}  
Iteration 4:{space 3}log pseudolikelihood = {res:-981.66821}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       757
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     65.85
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-981.66821{txt}{col 51}Pseudo R2{col 67}= {res}    0.0598

{txt}{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}         in_like_w11{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
disagree_total_close {c |}{col 22}{res}{space 2}-.0284427{col 34}{space 2} .0137288{col 45}{space 1}   -2.07{col 54}{space 3}0.038{col 62}{space 4}-.0553507{col 75}{space 3}-.0015347
{txt}{space 11}numgiven1 {c |}{col 22}{res}{space 2}-.0225423{col 34}{space 2} .2832013{col 45}{space 1}   -0.08{col 54}{space 3}0.937{col 62}{space 4}-.5776067{col 75}{space 3} .5325221
{txt}{space 4}network_interest {c |}{col 22}{res}{space 2}  .147266{col 34}{space 2}  .128163{col 45}{space 1}    1.15{col 54}{space 3}0.251{col 62}{space 4}-.1039289{col 75}{space 3} .3984609
{txt}{space 20} {c |}
{space 14}pid_21 {c |}
{space 11}Democrat  {c |}{col 22}{res}{space 2} .5915421{col 34}{space 2} .2169176{col 45}{space 1}    2.73{col 54}{space 3}0.006{col 62}{space 4} .1663915{col 75}{space 3} 1.016693
{txt}{space 9}interest_w1 {c |}{col 22}{res}{space 2} .4933569{col 34}{space 2} .1362622{col 45}{space 1}    3.62{col 54}{space 3}0.000{col 62}{space 4} .2262879{col 75}{space 3} .7604259
{txt}{space 20} {c |}
{space 14}gender {c |}
{space 13}Female  {c |}{col 22}{res}{space 2} .4467317{col 34}{space 2} .2265777{col 45}{space 1}    1.97{col 54}{space 3}0.049{col 62}{space 4} .0026477{col 75}{space 3} .8908158
{txt}{space 20} {c |}
{space 12}race_eth {c |}
{space 14}Black  {c |}{col 22}{res}{space 2} .5001772{col 34}{space 2} .4227491{col 45}{space 1}    1.18{col 54}{space 3}0.237{col 62}{space 4} -.328396{col 75}{space 3}  1.32875
{txt}{space 11}Hispanic  {c |}{col 22}{res}{space 2}-.5958021{col 34}{space 2} .4711513{col 45}{space 1}   -1.26{col 54}{space 3}0.206{col 62}{space 4}-1.519242{col 75}{space 3} .3276375
{txt}{space 14}Other  {c |}{col 22}{res}{space 2}-.2189361{col 34}{space 2} 1.277843{col 45}{space 1}   -0.17{col 54}{space 3}0.864{col 62}{space 4}-2.723462{col 75}{space 3}  2.28559
{txt}{space 20} {c |}
{space 17}age {c |}{col 22}{res}{space 2}-.0146621{col 34}{space 2} .0074455{col 45}{space 1}   -1.97{col 54}{space 3}0.049{col 62}{space 4}-.0292551{col 75}{space 3}-.0000692
{txt}{space 16}educ {c |}{col 22}{res}{space 2} .0153319{col 34}{space 2} .1141277{col 45}{space 1}    0.13{col 54}{space 3}0.893{col 62}{space 4}-.2083542{col 75}{space 3}  .239018
{txt}{space 14}income {c |}{col 22}{res}{space 2} .0028933{col 34}{space 2} .0305331{col 45}{space 1}    0.09{col 54}{space 3}0.925{col 62}{space 4}-.0569505{col 75}{space 3} .0627371
{txt}{space 20} {c |}
{space 13}marital {c |}
{space 12}Married  {c |}{col 22}{res}{space 2} .2051523{col 34}{space 2} .2978647{col 45}{space 1}    0.69{col 54}{space 3}0.491{col 62}{space 4}-.3786517{col 75}{space 3} .7889564
{txt}{space 11}evaluate1 {c |}{col 22}{res}{space 2} .2876234{col 34}{space 2}  .310392{col 45}{space 1}    0.93{col 54}{space 3}0.354{col 62}{space 4}-.3207337{col 75}{space 3} .8959805
{txt}{space 16}nfc1 {c |}{col 22}{res}{space 2}-.1166472{col 34}{space 2} .1702388{col 45}{space 1}   -0.69{col 54}{space 3}0.493{col 62}{space 4}-.4503092{col 75}{space 3} .2170148
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
               /cut1 {c |}{col 22}{res}{space 2} .9288753{col 34}{space 2} 1.163211{col 62}{space 4}-1.350976{col 75}{space 3} 3.208727
{txt}               /cut2 {c |}{col 22}{res}{space 2} 1.370515{col 34}{space 2} 1.163139{col 62}{space 4}-.9091956{col 75}{space 3} 3.650227
{txt}               /cut3 {c |}{col 22}{res}{space 2} 3.080093{col 34}{space 2} 1.182872{col 62}{space 4}  .761707{col 75}{space 3}  5.39848
{txt}               /cut4 {c |}{col 22}{res}{space 2} 4.802452{col 34}{space 2} 1.243804{col 62}{space 4} 2.364641{col 75}{space 3} 7.240263
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1044.0756}  
Iteration 1:{space 3}log pseudolikelihood = {res:  -983.201}  
Iteration 2:{space 3}log pseudolikelihood = {res:-981.90636}  
Iteration 3:{space 3}log pseudolikelihood = {res: -981.9045}  
Iteration 4:{space 3}log pseudolikelihood = {res: -981.9045}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       757
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     64.22
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res} -981.9045{txt}{col 51}Pseudo R2{col 67}= {res}    0.0595

{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}    Robust
{col 1}          in_like_w11{col 23}{c |}      Coef.{col 35}   Std. Err.{col 47}      z{col 55}   P>|z|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
disagree_total_weight {c |}{col 23}{res}{space 2}-.0314754{col 35}{space 2} .0141235{col 46}{space 1}   -2.23{col 55}{space 3}0.026{col 63}{space 4}-.0591569{col 76}{space 3} -.003794
{txt}{space 12}numgiven1 {c |}{col 23}{res}{space 2}-.0403585{col 35}{space 2} .2852896{col 46}{space 1}   -0.14{col 55}{space 3}0.888{col 63}{space 4}-.5995158{col 76}{space 3} .5187987
{txt}{space 5}network_interest {c |}{col 23}{res}{space 2} .1434788{col 35}{space 2} .1274093{col 46}{space 1}    1.13{col 55}{space 3}0.260{col 63}{space 4}-.1062389{col 76}{space 3} .3931965
{txt}{space 21} {c |}
{space 15}pid_21 {c |}
{space 12}Democrat  {c |}{col 23}{res}{space 2} .5934214{col 35}{space 2}  .215707{col 46}{space 1}    2.75{col 55}{space 3}0.006{col 63}{space 4} .1706435{col 76}{space 3} 1.016199
{txt}{space 10}interest_w1 {c |}{col 23}{res}{space 2} .4946263{col 35}{space 2} .1362873{col 46}{space 1}    3.63{col 55}{space 3}0.000{col 63}{space 4} .2275081{col 76}{space 3} .7617445
{txt}{space 21} {c |}
{space 15}gender {c |}
{space 14}Female  {c |}{col 23}{res}{space 2} .4573191{col 35}{space 2} .2252856{col 46}{space 1}    2.03{col 55}{space 3}0.042{col 63}{space 4} .0157674{col 76}{space 3} .8988708
{txt}{space 21} {c |}
{space 13}race_eth {c |}
{space 15}Black  {c |}{col 23}{res}{space 2} .5120369{col 35}{space 2} .4242585{col 46}{space 1}    1.21{col 55}{space 3}0.227{col 63}{space 4}-.3194946{col 76}{space 3} 1.343568
{txt}{space 12}Hispanic  {c |}{col 23}{res}{space 2}-.6106756{col 35}{space 2} .4646889{col 46}{space 1}   -1.31{col 55}{space 3}0.189{col 63}{space 4}-1.521449{col 76}{space 3}  .300098
{txt}{space 15}Other  {c |}{col 23}{res}{space 2}-.2249145{col 35}{space 2} 1.243797{col 46}{space 1}   -0.18{col 55}{space 3}0.857{col 63}{space 4}-2.662713{col 76}{space 3} 2.212884
{txt}{space 21} {c |}
{space 18}age {c |}{col 23}{res}{space 2}-.0151346{col 35}{space 2} .0074341{col 46}{space 1}   -2.04{col 55}{space 3}0.042{col 63}{space 4}-.0297052{col 76}{space 3}-.0005641
{txt}{space 17}educ {c |}{col 23}{res}{space 2} .0234398{col 35}{space 2} .1139114{col 46}{space 1}    0.21{col 55}{space 3}0.837{col 63}{space 4}-.1998225{col 76}{space 3}  .246702
{txt}{space 15}income {c |}{col 23}{res}{space 2} .0012495{col 35}{space 2} .0302025{col 46}{space 1}    0.04{col 55}{space 3}0.967{col 63}{space 4}-.0579464{col 76}{space 3} .0604454
{txt}{space 21} {c |}
{space 14}marital {c |}
{space 13}Married  {c |}{col 23}{res}{space 2} .2174286{col 35}{space 2} .2985729{col 46}{space 1}    0.73{col 55}{space 3}0.466{col 63}{space 4}-.3677635{col 76}{space 3} .8026208
{txt}{space 12}evaluate1 {c |}{col 23}{res}{space 2} .2815321{col 35}{space 2} .3090541{col 46}{space 1}    0.91{col 55}{space 3}0.362{col 63}{space 4}-.3242029{col 76}{space 3}  .887267
{txt}{space 17}nfc1 {c |}{col 23}{res}{space 2}-.1164003{col 35}{space 2} .1705565{col 46}{space 1}   -0.68{col 55}{space 3}0.495{col 63}{space 4}-.4506848{col 76}{space 3} .2178842
{txt}{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                /cut1 {c |}{col 23}{res}{space 2} .8981264{col 35}{space 2} 1.171828{col 63}{space 4}-1.398614{col 76}{space 3} 3.194866
{txt}                /cut2 {c |}{col 23}{res}{space 2}  1.33899{col 35}{space 2} 1.171263{col 63}{space 4}-.9566443{col 76}{space 3} 3.634624
{txt}                /cut3 {c |}{col 23}{res}{space 2} 3.047298{col 35}{space 2} 1.189944{col 63}{space 4} .7150503{col 76}{space 3} 5.379546
{txt}                /cut4 {c |}{col 23}{res}{space 2} 4.771374{col 35}{space 2}  1.25063{col 63}{space 4} 2.320185{col 76}{space 3} 7.222564
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est5{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1044.7635}  
Iteration 1:{space 3}log pseudolikelihood = {res:-986.93649}  
Iteration 2:{space 3}log pseudolikelihood = {res: -985.7826}  
Iteration 3:{space 3}log pseudolikelihood = {res:-985.78109}  
Iteration 4:{space 3}log pseudolikelihood = {res:-985.78109}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       758
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     62.39
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-985.78109{txt}{col 51}Pseudo R2{col 67}= {res}    0.0565

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}     in_like_w11{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}gendiff {c |}{col 18}{res}{space 2}-.1358572{col 30}{space 2}  .134554{col 41}{space 1}   -1.01{col 50}{space 3}0.313{col 58}{space 4}-.3995782{col 71}{space 3} .1278638
{txt}{space 7}numgiven1 {c |}{col 18}{res}{space 2} .0293128{col 30}{space 2} .2936422{col 41}{space 1}    0.10{col 50}{space 3}0.920{col 58}{space 4}-.5462153{col 71}{space 3} .6048409
{txt}network_interest {c |}{col 18}{res}{space 2} .1442495{col 30}{space 2} .1288111{col 41}{space 1}    1.12{col 50}{space 3}0.263{col 58}{space 4}-.1082156{col 71}{space 3} .3967146
{txt}{space 16} {c |}
{space 10}pid_21 {c |}
{space 7}Democrat  {c |}{col 18}{res}{space 2} .5802518{col 30}{space 2} .2111632{col 41}{space 1}    2.75{col 50}{space 3}0.006{col 58}{space 4} .1663794{col 71}{space 3} .9941241
{txt}{space 5}interest_w1 {c |}{col 18}{res}{space 2} .4979297{col 30}{space 2} .1331339{col 41}{space 1}    3.74{col 50}{space 3}0.000{col 58}{space 4} .2369921{col 71}{space 3} .7588674
{txt}{space 16} {c |}
{space 10}gender {c |}
{space 9}Female  {c |}{col 18}{res}{space 2} .5065009{col 30}{space 2} .2197335{col 41}{space 1}    2.31{col 50}{space 3}0.021{col 58}{space 4} .0758312{col 71}{space 3} .9371706
{txt}{space 16} {c |}
{space 8}race_eth {c |}
{space 10}Black  {c |}{col 18}{res}{space 2} .5657463{col 30}{space 2} .4180552{col 41}{space 1}    1.35{col 50}{space 3}0.176{col 58}{space 4}-.2536268{col 71}{space 3} 1.385119
{txt}{space 7}Hispanic  {c |}{col 18}{res}{space 2}-.6569175{col 30}{space 2} .4918053{col 41}{space 1}   -1.34{col 50}{space 3}0.182{col 58}{space 4}-1.620838{col 71}{space 3} .3070032
{txt}{space 10}Other  {c |}{col 18}{res}{space 2}-.2383514{col 30}{space 2} 1.156167{col 41}{space 1}   -0.21{col 50}{space 3}0.837{col 58}{space 4}-2.504398{col 71}{space 3} 2.027695
{txt}{space 16} {c |}
{space 13}age {c |}{col 18}{res}{space 2} -.015937{col 30}{space 2} .0073636{col 41}{space 1}   -2.16{col 50}{space 3}0.030{col 58}{space 4}-.0303694{col 71}{space 3}-.0015047
{txt}{space 12}educ {c |}{col 18}{res}{space 2} .0331998{col 30}{space 2} .1120237{col 41}{space 1}    0.30{col 50}{space 3}0.767{col 58}{space 4}-.1863626{col 71}{space 3} .2527622
{txt}{space 10}income {c |}{col 18}{res}{space 2} -.000581{col 30}{space 2} .0297363{col 41}{space 1}   -0.02{col 50}{space 3}0.984{col 58}{space 4}-.0588631{col 71}{space 3} .0577011
{txt}{space 16} {c |}
{space 9}marital {c |}
{space 8}Married  {c |}{col 18}{res}{space 2} .2214979{col 30}{space 2} .2981126{col 41}{space 1}    0.74{col 50}{space 3}0.457{col 58}{space 4}-.3627921{col 71}{space 3} .8057879
{txt}{space 7}evaluate1 {c |}{col 18}{res}{space 2} .3139433{col 30}{space 2} .3103747{col 41}{space 1}    1.01{col 50}{space 3}0.312{col 58}{space 4}-.2943799{col 71}{space 3} .9222665
{txt}{space 12}nfc1 {c |}{col 18}{res}{space 2}-.1242537{col 30}{space 2} .1711991{col 41}{space 1}   -0.73{col 50}{space 3}0.468{col 58}{space 4}-.4597978{col 71}{space 3} .2112904
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
           /cut1 {c |}{col 18}{res}{space 2} .6712021{col 30}{space 2} 1.232858{col 58}{space 4}-1.745156{col 71}{space 3}  3.08756
{txt}           /cut2 {c |}{col 18}{res}{space 2} 1.111969{col 30}{space 2} 1.230899{col 58}{space 4}-1.300549{col 71}{space 3} 3.524486
{txt}           /cut3 {c |}{col 18}{res}{space 2} 2.809487{col 30}{space 2} 1.245707{col 58}{space 4} .3679461{col 71}{space 3} 5.251028
{txt}           /cut4 {c |}{col 18}{res}{space 2} 4.522619{col 30}{space 2} 1.300318{col 58}{space 4} 1.974042{col 71}{space 3} 7.071197
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est6{txt} stored)

{com}.         
. esttab using 2008AMBIV_ALTMEASURES_INLIKE.rtf, onecell nobaselevels replace label pr2 aic bic se star(+ 0.10 * 0.05 ** 0.01) ///
>         mtitles("Original Measure" "(/D+A)" "Weigh: by Disc. Interest" ///
>                 "Weigh: by Tie Strength" "Weighted by Gen Dis" "Gen Disagree")  ///
>         title({c -(}\b Table XX.{c )-} "Alternative Measures of Disagreement - 2008-2009 ANES (AMBIVALENCE: IN-LIKES)") ///
>         rename(disagree_total Disagreement disagree_avg "Disagreement" disagree_total_int Disagreement disagree_total_close Disagreement ///
>                                 disagree_total_weight Disagreement gendiff Disagreement) 
{res}{txt}(output written to {browse  `"2008AMBIV_ALTMEASURES_INLIKE.rtf"'})

{com}. 
. 
. *out dislike
. 
. eststo clear
{txt}
{com}. foreach var in disagree_total disagree_avg disagree_total_int disagree_total_close  ///
>         disagree_total_weight gendiff  {c -(}
{txt}  2{com}. eststo: ologit out_dislike_w11 `var'  numgiven1 network_interest  i.pid_21 interest_w1 i.gender ///
>                         i.race age educ income i.marital evaluate1 nfc1 [pweight= WGTC11]
{txt}  3{com}.         {c )-}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1080.0244}  
Iteration 1:{space 3}log pseudolikelihood = {res:-989.58139}  
Iteration 2:{space 3}log pseudolikelihood = {res:-988.03318}  
Iteration 3:{space 3}log pseudolikelihood = {res:-988.02943}  
Iteration 4:{space 3}log pseudolikelihood = {res:-988.02943}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       753
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}    109.64
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-988.02943{txt}{col 51}Pseudo R2{col 67}= {res}    0.0852

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1} out_dislike_w11{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}disagree_total {c |}{col 18}{res}{space 2}-.0623252{col 30}{space 2} .0467844{col 41}{space 1}   -1.33{col 50}{space 3}0.183{col 58}{space 4}-.1540211{col 71}{space 3} .0293706
{txt}{space 7}numgiven1 {c |}{col 18}{res}{space 2}-.0819201{col 30}{space 2} .2170181{col 41}{space 1}   -0.38{col 50}{space 3}0.706{col 58}{space 4}-.5072677{col 71}{space 3} .3434275
{txt}network_interest {c |}{col 18}{res}{space 2}  .121952{col 30}{space 2}  .139644{col 41}{space 1}    0.87{col 50}{space 3}0.382{col 58}{space 4}-.1517452{col 71}{space 3} .3956493
{txt}{space 16} {c |}
{space 10}pid_21 {c |}
{space 7}Democrat  {c |}{col 18}{res}{space 2} .0128363{col 30}{space 2} .1955902{col 41}{space 1}    0.07{col 50}{space 3}0.948{col 58}{space 4}-.3705134{col 71}{space 3}  .396186
{txt}{space 5}interest_w1 {c |}{col 18}{res}{space 2} .5971813{col 30}{space 2} .1350465{col 41}{space 1}    4.42{col 50}{space 3}0.000{col 58}{space 4}  .332495{col 71}{space 3} .8618676
{txt}{space 16} {c |}
{space 10}gender {c |}
{space 9}Female  {c |}{col 18}{res}{space 2}-.2030976{col 30}{space 2} .2103866{col 41}{space 1}   -0.97{col 50}{space 3}0.334{col 58}{space 4}-.6154478{col 71}{space 3} .2092527
{txt}{space 16} {c |}
{space 8}race_eth {c |}
{space 10}Black  {c |}{col 18}{res}{space 2}-.8105431{col 30}{space 2} .5166565{col 41}{space 1}   -1.57{col 50}{space 3}0.117{col 58}{space 4}-1.823171{col 71}{space 3} .2020849
{txt}{space 7}Hispanic  {c |}{col 18}{res}{space 2}-.6185374{col 30}{space 2} .4133544{col 41}{space 1}   -1.50{col 50}{space 3}0.135{col 58}{space 4}-1.428697{col 71}{space 3} .1916224
{txt}{space 10}Other  {c |}{col 18}{res}{space 2}  2.01244{col 30}{space 2} .7741274{col 41}{space 1}    2.60{col 50}{space 3}0.009{col 58}{space 4} .4951785{col 71}{space 3} 3.529702
{txt}{space 16} {c |}
{space 13}age {c |}{col 18}{res}{space 2}-.0048877{col 30}{space 2} .0062428{col 41}{space 1}   -0.78{col 50}{space 3}0.434{col 58}{space 4}-.0171233{col 71}{space 3} .0073478
{txt}{space 12}educ {c |}{col 18}{res}{space 2} .1757885{col 30}{space 2} .1107895{col 41}{space 1}    1.59{col 50}{space 3}0.113{col 58}{space 4}-.0413549{col 71}{space 3}  .392932
{txt}{space 10}income {c |}{col 18}{res}{space 2}  .046856{col 30}{space 2} .0296018{col 41}{space 1}    1.58{col 50}{space 3}0.113{col 58}{space 4}-.0111625{col 71}{space 3} .1048745
{txt}{space 16} {c |}
{space 9}marital {c |}
{space 8}Married  {c |}{col 18}{res}{space 2}-.2311243{col 30}{space 2} .2509688{col 41}{space 1}   -0.92{col 50}{space 3}0.357{col 58}{space 4}-.7230141{col 71}{space 3} .2607656
{txt}{space 7}evaluate1 {c |}{col 18}{res}{space 2} 1.052313{col 30}{space 2}  .291038{col 41}{space 1}    3.62{col 50}{space 3}0.000{col 58}{space 4} .4818894{col 71}{space 3} 1.622737
{txt}{space 12}nfc1 {c |}{col 18}{res}{space 2}-.1702147{col 30}{space 2} .1576331{col 41}{space 1}   -1.08{col 50}{space 3}0.280{col 58}{space 4}  -.47917{col 71}{space 3} .1387406
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
           /cut1 {c |}{col 18}{res}{space 2} 2.582311{col 30}{space 2} 1.069259{col 58}{space 4} .4866019{col 71}{space 3} 4.678021
{txt}           /cut2 {c |}{col 18}{res}{space 2} 3.032737{col 30}{space 2} 1.063234{col 58}{space 4}  .948837{col 71}{space 3} 5.116636
{txt}           /cut3 {c |}{col 18}{res}{space 2} 4.343337{col 30}{space 2} 1.086084{col 58}{space 4} 2.214652{col 71}{space 3} 6.472022
{txt}           /cut4 {c |}{col 18}{res}{space 2}  5.90341{col 30}{space 2} 1.117309{col 58}{space 4} 3.713524{col 71}{space 3} 8.093296
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1080.0244}  
Iteration 1:{space 3}log pseudolikelihood = {res:-988.28954}  
Iteration 2:{space 3}log pseudolikelihood = {res:-986.70904}  
Iteration 3:{space 3}log pseudolikelihood = {res:-986.70517}  
Iteration 4:{space 3}log pseudolikelihood = {res:-986.70517}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       753
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}    111.40
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-986.70517{txt}{col 51}Pseudo R2{col 67}= {res}    0.0864

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1} out_dislike_w11{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}disagree_avg {c |}{col 18}{res}{space 2}-.2400617{col 30}{space 2} .1385333{col 41}{space 1}   -1.73{col 50}{space 3}0.083{col 58}{space 4}-.5115819{col 71}{space 3} .0314585
{txt}{space 7}numgiven1 {c |}{col 18}{res}{space 2}-.0794058{col 30}{space 2} .2122068{col 41}{space 1}   -0.37{col 50}{space 3}0.708{col 58}{space 4}-.4953236{col 71}{space 3}  .336512
{txt}network_interest {c |}{col 18}{res}{space 2} .1201503{col 30}{space 2} .1407893{col 41}{space 1}    0.85{col 50}{space 3}0.393{col 58}{space 4}-.1557917{col 71}{space 3} .3960924
{txt}{space 16} {c |}
{space 10}pid_21 {c |}
{space 7}Democrat  {c |}{col 18}{res}{space 2}  .007364{col 30}{space 2} .1956756{col 41}{space 1}    0.04{col 50}{space 3}0.970{col 58}{space 4} -.376153{col 71}{space 3}  .390881
{txt}{space 5}interest_w1 {c |}{col 18}{res}{space 2} .5987327{col 30}{space 2}  .135325{col 41}{space 1}    4.42{col 50}{space 3}0.000{col 58}{space 4} .3335005{col 71}{space 3} .8639649
{txt}{space 16} {c |}
{space 10}gender {c |}
{space 9}Female  {c |}{col 18}{res}{space 2}-.2187771{col 30}{space 2} .2108656{col 41}{space 1}   -1.04{col 50}{space 3}0.299{col 58}{space 4} -.632066{col 71}{space 3} .1945118
{txt}{space 16} {c |}
{space 8}race_eth {c |}
{space 10}Black  {c |}{col 18}{res}{space 2}-.8108404{col 30}{space 2} .5208567{col 41}{space 1}   -1.56{col 50}{space 3}0.120{col 58}{space 4}-1.831701{col 71}{space 3} .2100201
{txt}{space 7}Hispanic  {c |}{col 18}{res}{space 2}-.6044285{col 30}{space 2} .3979926{col 41}{space 1}   -1.52{col 50}{space 3}0.129{col 58}{space 4} -1.38448{col 71}{space 3} .1756226
{txt}{space 10}Other  {c |}{col 18}{res}{space 2} 2.042024{col 30}{space 2} .7994901{col 41}{space 1}    2.55{col 50}{space 3}0.011{col 58}{space 4} .4750522{col 71}{space 3} 3.608996
{txt}{space 16} {c |}
{space 13}age {c |}{col 18}{res}{space 2}-.0045214{col 30}{space 2} .0062429{col 41}{space 1}   -0.72{col 50}{space 3}0.469{col 58}{space 4}-.0167573{col 71}{space 3} .0077145
{txt}{space 12}educ {c |}{col 18}{res}{space 2} .1705039{col 30}{space 2} .1120172{col 41}{space 1}    1.52{col 50}{space 3}0.128{col 58}{space 4}-.0490456{col 71}{space 3} .3900535
{txt}{space 10}income {c |}{col 18}{res}{space 2} .0468562{col 30}{space 2} .0297143{col 41}{space 1}    1.58{col 50}{space 3}0.115{col 58}{space 4}-.0113829{col 71}{space 3} .1050952
{txt}{space 16} {c |}
{space 9}marital {c |}
{space 8}Married  {c |}{col 18}{res}{space 2}-.2255885{col 30}{space 2} .2510648{col 41}{space 1}   -0.90{col 50}{space 3}0.369{col 58}{space 4}-.7176664{col 71}{space 3} .2664893
{txt}{space 7}evaluate1 {c |}{col 18}{res}{space 2} 1.043025{col 30}{space 2} .2902067{col 41}{space 1}    3.59{col 50}{space 3}0.000{col 58}{space 4} .4742307{col 71}{space 3}  1.61182
{txt}{space 12}nfc1 {c |}{col 18}{res}{space 2}-.1645361{col 30}{space 2} .1579908{col 41}{space 1}   -1.04{col 50}{space 3}0.298{col 58}{space 4}-.4741923{col 71}{space 3} .1451202
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
           /cut1 {c |}{col 18}{res}{space 2} 2.599467{col 30}{space 2} 1.061612{col 58}{space 4} .5187468{col 71}{space 3} 4.680188
{txt}           /cut2 {c |}{col 18}{res}{space 2} 3.050721{col 30}{space 2}  1.05587{col 58}{space 4} .9812541{col 71}{space 3} 5.120188
{txt}           /cut3 {c |}{col 18}{res}{space 2} 4.363237{col 30}{space 2} 1.078244{col 58}{space 4} 2.249918{col 71}{space 3} 6.476555
{txt}           /cut4 {c |}{col 18}{res}{space 2} 5.926731{col 30}{space 2} 1.108067{col 58}{space 4} 3.754958{col 71}{space 3} 8.098503
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1080.0244}  
Iteration 1:{space 3}log pseudolikelihood = {res:-988.10699}  
Iteration 2:{space 3}log pseudolikelihood = {res:-986.50891}  
Iteration 3:{space 3}log pseudolikelihood = {res:-986.50492}  
Iteration 4:{space 3}log pseudolikelihood = {res:-986.50492}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       753
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}    113.64
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-986.50492{txt}{col 51}Pseudo R2{col 67}= {res}    0.0866

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}   out_dislike_w11{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      z{col 52}   P>|z|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
disagree_total_int {c |}{col 20}{res}{space 2}-.0237512{col 32}{space 2}  .012005{col 43}{space 1}   -1.98{col 52}{space 3}0.048{col 60}{space 4}-.0472806{col 73}{space 3}-.0002219
{txt}{space 9}numgiven1 {c |}{col 20}{res}{space 2}-.0947809{col 32}{space 2} .2156614{col 43}{space 1}   -0.44{col 52}{space 3}0.660{col 60}{space 4}-.5174696{col 73}{space 3} .3279077
{txt}{space 2}network_interest {c |}{col 20}{res}{space 2} .0959527{col 32}{space 2} .1389916{col 43}{space 1}    0.69{col 52}{space 3}0.490{col 60}{space 4}-.1764658{col 73}{space 3} .3683712
{txt}{space 18} {c |}
{space 12}pid_21 {c |}
{space 9}Democrat  {c |}{col 20}{res}{space 2} .0087008{col 32}{space 2} .1959984{col 43}{space 1}    0.04{col 52}{space 3}0.965{col 60}{space 4} -.375449{col 73}{space 3} .3928505
{txt}{space 7}interest_w1 {c |}{col 20}{res}{space 2} .5932159{col 32}{space 2} .1348577{col 43}{space 1}    4.40{col 52}{space 3}0.000{col 60}{space 4} .3288997{col 73}{space 3} .8575322
{txt}{space 18} {c |}
{space 12}gender {c |}
{space 11}Female  {c |}{col 20}{res}{space 2}-.2138221{col 32}{space 2} .2095128{col 43}{space 1}   -1.02{col 52}{space 3}0.307{col 60}{space 4}-.6244597{col 73}{space 3} .1968156
{txt}{space 18} {c |}
{space 10}race_eth {c |}
{space 12}Black  {c |}{col 20}{res}{space 2}-.8190135{col 32}{space 2}  .518363{col 43}{space 1}   -1.58{col 52}{space 3}0.114{col 60}{space 4}-1.834986{col 73}{space 3} .1969593
{txt}{space 9}Hispanic  {c |}{col 20}{res}{space 2}-.6102575{col 32}{space 2} .4091165{col 43}{space 1}   -1.49{col 52}{space 3}0.136{col 60}{space 4}-1.412111{col 73}{space 3}  .191596
{txt}{space 12}Other  {c |}{col 20}{res}{space 2} 2.029895{col 32}{space 2} .7906378{col 43}{space 1}    2.57{col 52}{space 3}0.010{col 60}{space 4} .4802736{col 73}{space 3} 3.579517
{txt}{space 18} {c |}
{space 15}age {c |}{col 20}{res}{space 2}-.0042625{col 32}{space 2} .0062154{col 43}{space 1}   -0.69{col 52}{space 3}0.493{col 60}{space 4}-.0164444{col 73}{space 3} .0079195
{txt}{space 14}educ {c |}{col 20}{res}{space 2} .1715558{col 32}{space 2} .1117844{col 43}{space 1}    1.53{col 52}{space 3}0.125{col 60}{space 4}-.0475376{col 73}{space 3} .3906492
{txt}{space 12}income {c |}{col 20}{res}{space 2} .0484184{col 32}{space 2} .0300338{col 43}{space 1}    1.61{col 52}{space 3}0.107{col 60}{space 4}-.0104467{col 73}{space 3} .1072836
{txt}{space 18} {c |}
{space 11}marital {c |}
{space 10}Married  {c |}{col 20}{res}{space 2}-.2291928{col 32}{space 2} .2509193{col 43}{space 1}   -0.91{col 52}{space 3}0.361{col 60}{space 4}-.7209855{col 73}{space 3} .2625999
{txt}{space 9}evaluate1 {c |}{col 20}{res}{space 2} 1.040723{col 32}{space 2} .2907902{col 43}{space 1}    3.58{col 52}{space 3}0.000{col 60}{space 4} .4707847{col 73}{space 3} 1.610661
{txt}{space 14}nfc1 {c |}{col 20}{res}{space 2}-.1629393{col 32}{space 2} .1577942{col 43}{space 1}   -1.03{col 52}{space 3}0.302{col 60}{space 4}-.4722103{col 73}{space 3} .1463316
{txt}{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
             /cut1 {c |}{col 20}{res}{space 2} 2.488433{col 32}{space 2} 1.069976{col 60}{space 4} .3913184{col 73}{space 3} 4.585548
{txt}             /cut2 {c |}{col 20}{res}{space 2} 2.938571{col 32}{space 2} 1.063958{col 60}{space 4} .8532522{col 73}{space 3}  5.02389
{txt}             /cut3 {c |}{col 20}{res}{space 2} 4.251415{col 32}{space 2}  1.08637{col 60}{space 4} 2.122169{col 73}{space 3}  6.38066
{txt}             /cut4 {c |}{col 20}{res}{space 2} 5.818832{col 32}{space 2} 1.118021{col 60}{space 4} 3.627552{col 73}{space 3} 8.010112
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1080.0244}  
Iteration 1:{space 3}log pseudolikelihood = {res:-989.71606}  
Iteration 2:{space 3}log pseudolikelihood = {res:-988.17301}  
Iteration 3:{space 3}log pseudolikelihood = {res:-988.16929}  
Iteration 4:{space 3}log pseudolikelihood = {res:-988.16929}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       753
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}    110.19
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-988.16929{txt}{col 51}Pseudo R2{col 67}= {res}    0.0850

{txt}{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}     out_dislike_w11{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
disagree_total_close {c |}{col 22}{res}{space 2}-.0144963{col 34}{space 2} .0114843{col 45}{space 1}   -1.26{col 54}{space 3}0.207{col 62}{space 4}-.0370052{col 75}{space 3} .0080125
{txt}{space 11}numgiven1 {c |}{col 22}{res}{space 2}-.0783036{col 34}{space 2} .2175376{col 45}{space 1}   -0.36{col 54}{space 3}0.719{col 62}{space 4}-.5046695{col 75}{space 3} .3480623
{txt}{space 4}network_interest {c |}{col 22}{res}{space 2} .1222117{col 34}{space 2} .1394903{col 45}{space 1}    0.88{col 54}{space 3}0.381{col 62}{space 4}-.1511843{col 75}{space 3} .3956077
{txt}{space 20} {c |}
{space 14}pid_21 {c |}
{space 11}Democrat  {c |}{col 22}{res}{space 2} .0143558{col 34}{space 2} .1959365{col 45}{space 1}    0.07{col 54}{space 3}0.942{col 62}{space 4}-.3696727{col 75}{space 3} .3983843
{txt}{space 9}interest_w1 {c |}{col 22}{res}{space 2} .5997998{col 34}{space 2} .1351222{col 45}{space 1}    4.44{col 54}{space 3}0.000{col 62}{space 4} .3349652{col 75}{space 3} .8646344
{txt}{space 20} {c |}
{space 14}gender {c |}
{space 13}Female  {c |}{col 22}{res}{space 2} -.202649{col 34}{space 2} .2087355{col 45}{space 1}   -0.97{col 54}{space 3}0.332{col 62}{space 4}-.6117629{col 75}{space 3}  .206465
{txt}{space 20} {c |}
{space 12}race_eth {c |}
{space 14}Black  {c |}{col 22}{res}{space 2}-.8142143{col 34}{space 2} .5183041{col 45}{space 1}   -1.57{col 54}{space 3}0.116{col 62}{space 4}-1.830072{col 75}{space 3} .2016431
{txt}{space 11}Hispanic  {c |}{col 22}{res}{space 2} -.603475{col 34}{space 2} .4162658{col 45}{space 1}   -1.45{col 54}{space 3}0.147{col 62}{space 4}-1.419341{col 75}{space 3} .2123911
{txt}{space 14}Other  {c |}{col 22}{res}{space 2} 2.006319{col 34}{space 2} .7742488{col 45}{space 1}    2.59{col 54}{space 3}0.010{col 62}{space 4} .4888188{col 75}{space 3} 3.523818
{txt}{space 20} {c |}
{space 17}age {c |}{col 22}{res}{space 2} -.004928{col 34}{space 2} .0062857{col 45}{space 1}   -0.78{col 54}{space 3}0.433{col 62}{space 4}-.0172478{col 75}{space 3} .0073918
{txt}{space 16}educ {c |}{col 22}{res}{space 2} .1759277{col 34}{space 2} .1106067{col 45}{space 1}    1.59{col 54}{space 3}0.112{col 62}{space 4}-.0408575{col 75}{space 3} .3927128
{txt}{space 14}income {c |}{col 22}{res}{space 2} .0473874{col 34}{space 2} .0296262{col 45}{space 1}    1.60{col 54}{space 3}0.110{col 62}{space 4}-.0106788{col 75}{space 3} .1054536
{txt}{space 20} {c |}
{space 13}marital {c |}
{space 12}Married  {c |}{col 22}{res}{space 2}-.2337717{col 34}{space 2} .2518599{col 45}{space 1}   -0.93{col 54}{space 3}0.353{col 62}{space 4}-.7274081{col 75}{space 3} .2598646
{txt}{space 11}evaluate1 {c |}{col 22}{res}{space 2} 1.050655{col 34}{space 2} .2930126{col 45}{space 1}    3.59{col 54}{space 3}0.000{col 62}{space 4}  .476361{col 75}{space 3} 1.624949
{txt}{space 16}nfc1 {c |}{col 22}{res}{space 2} -.171369{col 34}{space 2} .1577768{col 45}{space 1}   -1.09{col 54}{space 3}0.277{col 62}{space 4}-.4806059{col 75}{space 3} .1378679
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
               /cut1 {c |}{col 22}{res}{space 2} 2.606352{col 34}{space 2} 1.064738{col 62}{space 4} .5195037{col 75}{space 3} 4.693199
{txt}               /cut2 {c |}{col 22}{res}{space 2} 3.057144{col 34}{space 2} 1.059044{col 62}{space 4} .9814568{col 75}{space 3} 5.132831
{txt}               /cut3 {c |}{col 22}{res}{space 2} 4.367406{col 34}{space 2} 1.082144{col 62}{space 4} 2.246443{col 75}{space 3}  6.48837
{txt}               /cut4 {c |}{col 22}{res}{space 2} 5.926257{col 34}{space 2} 1.113573{col 62}{space 4} 3.743693{col 75}{space 3} 8.108821
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1080.0244}  
Iteration 1:{space 3}log pseudolikelihood = {res:-987.25465}  
Iteration 2:{space 3}log pseudolikelihood = {res:-985.63659}  
Iteration 3:{space 3}log pseudolikelihood = {res:-985.63241}  
Iteration 4:{space 3}log pseudolikelihood = {res:-985.63241}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       753
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}    110.66
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-985.63241{txt}{col 51}Pseudo R2{col 67}= {res}    0.0874

{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}    Robust
{col 1}      out_dislike_w11{col 23}{c |}      Coef.{col 35}   Std. Err.{col 47}      z{col 55}   P>|z|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
disagree_total_weight {c |}{col 23}{res}{space 2}-.0289401{col 35}{space 2} .0128585{col 46}{space 1}   -2.25{col 55}{space 3}0.024{col 63}{space 4}-.0541422{col 76}{space 3}-.0037379
{txt}{space 12}numgiven1 {c |}{col 23}{res}{space 2}-.1187316{col 35}{space 2} .2172027{col 46}{space 1}   -0.55{col 55}{space 3}0.585{col 63}{space 4}-.5444412{col 76}{space 3} .3069779
{txt}{space 5}network_interest {c |}{col 23}{res}{space 2} .1185866{col 35}{space 2} .1392536{col 46}{space 1}    0.85{col 55}{space 3}0.394{col 63}{space 4}-.1543454{col 76}{space 3} .3915187
{txt}{space 21} {c |}
{space 15}pid_21 {c |}
{space 12}Democrat  {c |}{col 23}{res}{space 2} .0194178{col 35}{space 2} .1953558{col 46}{space 1}    0.10{col 55}{space 3}0.921{col 63}{space 4}-.3634725{col 76}{space 3} .4023081
{txt}{space 10}interest_w1 {c |}{col 23}{res}{space 2} .6009259{col 35}{space 2} .1337448{col 46}{space 1}    4.49{col 55}{space 3}0.000{col 63}{space 4}  .338791{col 76}{space 3} .8630609
{txt}{space 21} {c |}
{space 15}gender {c |}
{space 14}Female  {c |}{col 23}{res}{space 2}-.2252063{col 35}{space 2} .2105607{col 46}{space 1}   -1.07{col 55}{space 3}0.285{col 63}{space 4}-.6378977{col 76}{space 3} .1874852
{txt}{space 21} {c |}
{space 13}race_eth {c |}
{space 15}Black  {c |}{col 23}{res}{space 2}-.8419157{col 35}{space 2} .5294936{col 46}{space 1}   -1.59{col 55}{space 3}0.112{col 63}{space 4}-1.879704{col 76}{space 3} .1958728
{txt}{space 12}Hispanic  {c |}{col 23}{res}{space 2}-.5887386{col 35}{space 2} .4007715{col 46}{space 1}   -1.47{col 55}{space 3}0.142{col 63}{space 4}-1.374236{col 76}{space 3} .1967592
{txt}{space 15}Other  {c |}{col 23}{res}{space 2} 2.042015{col 35}{space 2}  .801616{col 46}{space 1}    2.55{col 55}{space 3}0.011{col 63}{space 4} .4708762{col 76}{space 3} 3.613153
{txt}{space 21} {c |}
{space 18}age {c |}{col 23}{res}{space 2} -.004696{col 35}{space 2} .0062401{col 46}{space 1}   -0.75{col 55}{space 3}0.452{col 63}{space 4}-.0169263{col 76}{space 3} .0075344
{txt}{space 17}educ {c |}{col 23}{res}{space 2} .1752733{col 35}{space 2} .1101866{col 46}{space 1}    1.59{col 55}{space 3}0.112{col 63}{space 4}-.0406884{col 76}{space 3}  .391235
{txt}{space 15}income {c |}{col 23}{res}{space 2} .0472939{col 35}{space 2} .0297383{col 46}{space 1}    1.59{col 55}{space 3}0.112{col 63}{space 4}-.0109921{col 76}{space 3}   .10558
{txt}{space 21} {c |}
{space 14}marital {c |}
{space 13}Married  {c |}{col 23}{res}{space 2} -.225916{col 35}{space 2} .2519037{col 46}{space 1}   -0.90{col 55}{space 3}0.370{col 63}{space 4}-.7196382{col 76}{space 3} .2678062
{txt}{space 12}evaluate1 {c |}{col 23}{res}{space 2} 1.034506{col 35}{space 2} .2898606{col 46}{space 1}    3.57{col 55}{space 3}0.000{col 63}{space 4} .4663899{col 76}{space 3} 1.602623
{txt}{space 17}nfc1 {c |}{col 23}{res}{space 2}-.1576055{col 35}{space 2} .1577253{col 46}{space 1}   -1.00{col 55}{space 3}0.318{col 63}{space 4}-.4667414{col 76}{space 3} .1515304
{txt}{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                /cut1 {c |}{col 23}{res}{space 2}  2.56743{col 35}{space 2} 1.064358{col 63}{space 4}  .481327{col 76}{space 3} 4.653533
{txt}                /cut2 {c |}{col 23}{res}{space 2} 3.018984{col 35}{space 2} 1.058573{col 63}{space 4} .9442189{col 76}{space 3}  5.09375
{txt}                /cut3 {c |}{col 23}{res}{space 2} 4.334158{col 35}{space 2} 1.081456{col 63}{space 4} 2.214542{col 76}{space 3} 6.453774
{txt}                /cut4 {c |}{col 23}{res}{space 2}  5.90331{col 35}{space 2} 1.113206{col 63}{space 4} 3.721467{col 76}{space 3} 8.085152
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est5{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1080.6958}  
Iteration 1:{space 3}log pseudolikelihood = {res:-981.36786}  
Iteration 2:{space 3}log pseudolikelihood = {res:-979.44119}  
Iteration 3:{space 3}log pseudolikelihood = {res:-979.43454}  
Iteration 4:{space 3}log pseudolikelihood = {res:-979.43454}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       754
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}    108.25
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-979.43454{txt}{col 51}Pseudo R2{col 67}= {res}    0.0937

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1} out_dislike_w11{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}gendiff {c |}{col 18}{res}{space 2}-.4212765{col 30}{space 2} .1596596{col 41}{space 1}   -2.64{col 50}{space 3}0.008{col 58}{space 4}-.7342036{col 71}{space 3}-.1083495
{txt}{space 7}numgiven1 {c |}{col 18}{res}{space 2}-.0964033{col 30}{space 2} .2269714{col 41}{space 1}   -0.42{col 50}{space 3}0.671{col 58}{space 4}-.5412591{col 71}{space 3} .3484526
{txt}network_interest {c |}{col 18}{res}{space 2} .1022178{col 30}{space 2} .1334981{col 41}{space 1}    0.77{col 50}{space 3}0.444{col 58}{space 4}-.1594337{col 71}{space 3} .3638693
{txt}{space 16} {c |}
{space 10}pid_21 {c |}
{space 7}Democrat  {c |}{col 18}{res}{space 2} .0541486{col 30}{space 2} .1947793{col 41}{space 1}    0.28{col 50}{space 3}0.781{col 58}{space 4}-.3276119{col 71}{space 3}  .435909
{txt}{space 5}interest_w1 {c |}{col 18}{res}{space 2} .6389483{col 30}{space 2} .1234805{col 41}{space 1}    5.17{col 50}{space 3}0.000{col 58}{space 4}  .396931{col 71}{space 3} .8809656
{txt}{space 16} {c |}
{space 10}gender {c |}
{space 9}Female  {c |}{col 18}{res}{space 2}-.2048297{col 30}{space 2} .2054578{col 41}{space 1}   -1.00{col 50}{space 3}0.319{col 58}{space 4}-.6075195{col 71}{space 3} .1978601
{txt}{space 16} {c |}
{space 8}race_eth {c |}
{space 10}Black  {c |}{col 18}{res}{space 2}-.9030166{col 30}{space 2} .5534162{col 41}{space 1}   -1.63{col 50}{space 3}0.103{col 58}{space 4}-1.987692{col 71}{space 3} .1816592
{txt}{space 7}Hispanic  {c |}{col 18}{res}{space 2}-.5616515{col 30}{space 2} .3988773{col 41}{space 1}   -1.41{col 50}{space 3}0.159{col 58}{space 4}-1.343437{col 71}{space 3} .2201336
{txt}{space 10}Other  {c |}{col 18}{res}{space 2} 1.904549{col 30}{space 2} .6762842{col 41}{space 1}    2.82{col 50}{space 3}0.005{col 58}{space 4} .5790559{col 71}{space 3} 3.230041
{txt}{space 16} {c |}
{space 13}age {c |}{col 18}{res}{space 2}-.0058166{col 30}{space 2} .0060325{col 41}{space 1}   -0.96{col 50}{space 3}0.335{col 58}{space 4}  -.01764{col 71}{space 3} .0060068
{txt}{space 12}educ {c |}{col 18}{res}{space 2} .2022576{col 30}{space 2} .1073338{col 41}{space 1}    1.88{col 50}{space 3}0.060{col 58}{space 4}-.0081127{col 71}{space 3} .4126279
{txt}{space 10}income {c |}{col 18}{res}{space 2}  .045568{col 30}{space 2} .0290431{col 41}{space 1}    1.57{col 50}{space 3}0.117{col 58}{space 4}-.0113554{col 71}{space 3} .1024913
{txt}{space 16} {c |}
{space 9}marital {c |}
{space 8}Married  {c |}{col 18}{res}{space 2}-.2156668{col 30}{space 2} .2520145{col 41}{space 1}   -0.86{col 50}{space 3}0.392{col 58}{space 4}-.7096062{col 71}{space 3} .2782726
{txt}{space 7}evaluate1 {c |}{col 18}{res}{space 2} 1.081222{col 30}{space 2} .2901403{col 41}{space 1}    3.73{col 50}{space 3}0.000{col 58}{space 4} .5125572{col 71}{space 3} 1.649886
{txt}{space 12}nfc1 {c |}{col 18}{res}{space 2}-.1446283{col 30}{space 2} .1560376{col 41}{space 1}   -0.93{col 50}{space 3}0.354{col 58}{space 4}-.4504562{col 71}{space 3} .1611997
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
           /cut1 {c |}{col 18}{res}{space 2} 1.712819{col 30}{space 2} 1.152302{col 58}{space 4}-.5456525{col 71}{space 3}  3.97129
{txt}           /cut2 {c |}{col 18}{res}{space 2} 2.169702{col 30}{space 2} 1.145055{col 58}{space 4}-.0745639{col 71}{space 3} 4.413968
{txt}           /cut3 {c |}{col 18}{res}{space 2} 3.506898{col 30}{space 2} 1.158343{col 58}{space 4} 1.236587{col 71}{space 3} 5.777208
{txt}           /cut4 {c |}{col 18}{res}{space 2} 5.102662{col 30}{space 2} 1.175222{col 58}{space 4} 2.799268{col 71}{space 3} 7.406056
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est6{txt} stored)

{com}.         
. esttab using 2008AMBIV_ALTMEASURES_OUTDISLIKE.rtf, onecell nobaselevels replace label pr2 aic bic se star(+ 0.10 * 0.05 ** 0.01) ///
>         mtitles("Original Measure" "(/D+A)" "Weigh: by Disc. Interest" ///
>                 "Weigh: by Tie Strength" "Weighted by Gen Dis" "Gen Disagree")  ///
>         title({c -(}\b Table XX.{c )-} "Alternative Measures of Disagreement - 2008-2009 ANES (AMBIVALENCE: OUT-DISLIKE)") ///
>         rename(disagree_total Disagreement disagree_avg "Disagreement" disagree_total_int Disagreement disagree_total_close Disagreement ///
>                                 disagree_total_weight Disagreement gendiff Disagreement) 
{res}{txt}(output written to {browse  `"2008AMBIV_ALTMEASURES_OUTDISLIKE.rtf"'})

{com}. 
. 
. *in dislike     
. eststo clear
{txt}
{com}. foreach var in disagree_total disagree_avg disagree_total_int disagree_total_close  ///
>         disagree_total_weight gendiff  {c -(}        
{txt}  2{com}. eststo: ologit in_dislike_w11 `var'  numgiven1 network_interest  i.pid_21 interest_w1 i.gender ///
>                         i.race age educ income i.marital evaluate1 nfc1 [pweight= WGTC11]
{txt}  3{com}. {c )-}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-824.51523}  
Iteration 1:{space 3}log pseudolikelihood = {res:-744.09182}  
Iteration 2:{space 3}log pseudolikelihood = {res:-741.66589}  
Iteration 3:{space 3}log pseudolikelihood = {res:-741.65782}  
Iteration 4:{space 3}log pseudolikelihood = {res:-741.65782}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       688
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     91.39
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-741.65782{txt}{col 51}Pseudo R2{col 67}= {res}    0.1005

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}  in_dislike_w11{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}disagree_total {c |}{col 18}{res}{space 2} .1016438{col 30}{space 2}  .050303{col 41}{space 1}    2.02{col 50}{space 3}0.043{col 58}{space 4} .0030518{col 71}{space 3} .2002358
{txt}{space 7}numgiven1 {c |}{col 18}{res}{space 2}-.5285336{col 30}{space 2} .2603346{col 41}{space 1}   -2.03{col 50}{space 3}0.042{col 58}{space 4} -1.03878{col 71}{space 3}-.0182871
{txt}network_interest {c |}{col 18}{res}{space 2}-.0207984{col 30}{space 2} .1499548{col 41}{space 1}   -0.14{col 50}{space 3}0.890{col 58}{space 4}-.3147043{col 71}{space 3} .2731075
{txt}{space 16} {c |}
{space 10}pid_21 {c |}
{space 7}Democrat  {c |}{col 18}{res}{space 2}-.8381537{col 30}{space 2} .2330531{col 41}{space 1}   -3.60{col 50}{space 3}0.000{col 58}{space 4}-1.294929{col 71}{space 3} -.381378
{txt}{space 5}interest_w1 {c |}{col 18}{res}{space 2} .2867896{col 30}{space 2} .1370513{col 41}{space 1}    2.09{col 50}{space 3}0.036{col 58}{space 4}  .018174{col 71}{space 3} .5554052
{txt}{space 16} {c |}
{space 10}gender {c |}
{space 9}Female  {c |}{col 18}{res}{space 2}-.2851114{col 30}{space 2} .2399061{col 41}{space 1}   -1.19{col 50}{space 3}0.235{col 58}{space 4}-.7553188{col 71}{space 3} .1850959
{txt}{space 16} {c |}
{space 8}race_eth {c |}
{space 10}Black  {c |}{col 18}{res}{space 2}-1.128376{col 30}{space 2} .5121429{col 41}{space 1}   -2.20{col 50}{space 3}0.028{col 58}{space 4}-2.132158{col 71}{space 3}-.1245945
{txt}{space 7}Hispanic  {c |}{col 18}{res}{space 2}-.3621488{col 30}{space 2} .5012606{col 41}{space 1}   -0.72{col 50}{space 3}0.470{col 58}{space 4}-1.344602{col 71}{space 3} .6203039
{txt}{space 10}Other  {c |}{col 18}{res}{space 2} .8920267{col 30}{space 2} .4884782{col 41}{space 1}    1.83{col 50}{space 3}0.068{col 58}{space 4} -.065373{col 71}{space 3} 1.849426
{txt}{space 16} {c |}
{space 13}age {c |}{col 18}{res}{space 2} .0194934{col 30}{space 2} .0079714{col 41}{space 1}    2.45{col 50}{space 3}0.014{col 58}{space 4} .0038697{col 71}{space 3} .0351171
{txt}{space 12}educ {c |}{col 18}{res}{space 2} .1611776{col 30}{space 2} .1477812{col 41}{space 1}    1.09{col 50}{space 3}0.275{col 58}{space 4}-.1284682{col 71}{space 3} .4508233
{txt}{space 10}income {c |}{col 18}{res}{space 2} .0333277{col 30}{space 2} .0323492{col 41}{space 1}    1.03{col 50}{space 3}0.303{col 58}{space 4}-.0300755{col 71}{space 3}  .096731
{txt}{space 16} {c |}
{space 9}marital {c |}
{space 8}Married  {c |}{col 18}{res}{space 2}-.4869925{col 30}{space 2} .2489821{col 41}{space 1}   -1.96{col 50}{space 3}0.050{col 58}{space 4}-.9749885{col 71}{space 3} .0010036
{txt}{space 7}evaluate1 {c |}{col 18}{res}{space 2} .6946222{col 30}{space 2} .3137012{col 41}{space 1}    2.21{col 50}{space 3}0.027{col 58}{space 4} .0797791{col 71}{space 3} 1.309465
{txt}{space 12}nfc1 {c |}{col 18}{res}{space 2} .0814464{col 30}{space 2} .1630076{col 41}{space 1}    0.50{col 50}{space 3}0.617{col 58}{space 4}-.2380427{col 71}{space 3} .4009355
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
           /cut1 {c |}{col 18}{res}{space 2} .9762348{col 30}{space 2} 1.063208{col 58}{space 4}-1.107614{col 71}{space 3} 3.060083
{txt}           /cut2 {c |}{col 18}{res}{space 2} 2.083496{col 30}{space 2} 1.085904{col 58}{space 4}-.0448363{col 71}{space 3} 4.211828
{txt}           /cut3 {c |}{col 18}{res}{space 2} 3.993381{col 30}{space 2} 1.145852{col 58}{space 4} 1.747553{col 71}{space 3} 6.239209
{txt}           /cut4 {c |}{col 18}{res}{space 2}  5.00634{col 30}{space 2}  1.28176{col 58}{space 4} 2.494136{col 71}{space 3} 7.518544
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-824.51523}  
Iteration 1:{space 3}log pseudolikelihood = {res: -744.6509}  
Iteration 2:{space 3}log pseudolikelihood = {res:-742.23784}  
Iteration 3:{space 3}log pseudolikelihood = {res:-742.22971}  
Iteration 4:{space 3}log pseudolikelihood = {res:-742.22971}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       688
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     91.26
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-742.22971{txt}{col 51}Pseudo R2{col 67}= {res}    0.0998

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}  in_dislike_w11{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}disagree_avg {c |}{col 18}{res}{space 2} .2482469{col 30}{space 2} .1551312{col 41}{space 1}    1.60{col 50}{space 3}0.110{col 58}{space 4}-.0558046{col 71}{space 3} .5522985
{txt}{space 7}numgiven1 {c |}{col 18}{res}{space 2}-.5425251{col 30}{space 2} .2601473{col 41}{space 1}   -2.09{col 50}{space 3}0.037{col 58}{space 4}-1.052405{col 71}{space 3}-.0326457
{txt}network_interest {c |}{col 18}{res}{space 2} -.019791{col 30}{space 2} .1499486{col 41}{space 1}   -0.13{col 50}{space 3}0.895{col 58}{space 4}-.3136849{col 71}{space 3} .2741029
{txt}{space 16} {c |}
{space 10}pid_21 {c |}
{space 7}Democrat  {c |}{col 18}{res}{space 2}-.8321874{col 30}{space 2} .2345688{col 41}{space 1}   -3.55{col 50}{space 3}0.000{col 58}{space 4}-1.291934{col 71}{space 3} -.372441
{txt}{space 5}interest_w1 {c |}{col 18}{res}{space 2} .2833271{col 30}{space 2} .1365997{col 41}{space 1}    2.07{col 50}{space 3}0.038{col 58}{space 4} .0155965{col 71}{space 3} .5510576
{txt}{space 16} {c |}
{space 10}gender {c |}
{space 9}Female  {c |}{col 18}{res}{space 2}-.2936804{col 30}{space 2} .2393442{col 41}{space 1}   -1.23{col 50}{space 3}0.220{col 58}{space 4}-.7627865{col 71}{space 3} .1754257
{txt}{space 16} {c |}
{space 8}race_eth {c |}
{space 10}Black  {c |}{col 18}{res}{space 2}-1.134151{col 30}{space 2} .5101153{col 41}{space 1}   -2.22{col 50}{space 3}0.026{col 58}{space 4}-2.133959{col 71}{space 3}-.1343435
{txt}{space 7}Hispanic  {c |}{col 18}{res}{space 2}-.3585635{col 30}{space 2}  .509994{col 41}{space 1}   -0.70{col 50}{space 3}0.482{col 58}{space 4}-1.358133{col 71}{space 3} .6410064
{txt}{space 10}Other  {c |}{col 18}{res}{space 2} .8958372{col 30}{space 2} .4816024{col 41}{space 1}    1.86{col 50}{space 3}0.063{col 58}{space 4}-.0480861{col 71}{space 3} 1.839761
{txt}{space 16} {c |}
{space 13}age {c |}{col 18}{res}{space 2} .0193033{col 30}{space 2} .0079597{col 41}{space 1}    2.43{col 50}{space 3}0.015{col 58}{space 4} .0037026{col 71}{space 3}  .034904
{txt}{space 12}educ {c |}{col 18}{res}{space 2} .1627482{col 30}{space 2} .1468621{col 41}{space 1}    1.11{col 50}{space 3}0.268{col 58}{space 4}-.1250961{col 71}{space 3} .4505926
{txt}{space 10}income {c |}{col 18}{res}{space 2} .0335507{col 30}{space 2} .0320435{col 41}{space 1}    1.05{col 50}{space 3}0.295{col 58}{space 4}-.0292535{col 71}{space 3} .0963549
{txt}{space 16} {c |}
{space 9}marital {c |}
{space 8}Married  {c |}{col 18}{res}{space 2}-.4910952{col 30}{space 2} .2476271{col 41}{space 1}   -1.98{col 50}{space 3}0.047{col 58}{space 4}-.9764355{col 71}{space 3} -.005755
{txt}{space 7}evaluate1 {c |}{col 18}{res}{space 2} .6864935{col 30}{space 2}  .310571{col 41}{space 1}    2.21{col 50}{space 3}0.027{col 58}{space 4} .0777855{col 71}{space 3} 1.295202
{txt}{space 12}nfc1 {c |}{col 18}{res}{space 2} .0812606{col 30}{space 2} .1622885{col 41}{space 1}    0.50{col 50}{space 3}0.617{col 58}{space 4} -.236819{col 71}{space 3} .3993402
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
           /cut1 {c |}{col 18}{res}{space 2} .9275104{col 30}{space 2} 1.056895{col 58}{space 4}-1.143965{col 71}{space 3} 2.998986
{txt}           /cut2 {c |}{col 18}{res}{space 2} 2.033399{col 30}{space 2} 1.077287{col 58}{space 4}-.0780449{col 71}{space 3} 4.144842
{txt}           /cut3 {c |}{col 18}{res}{space 2} 3.943321{col 30}{space 2} 1.136722{col 58}{space 4} 1.715387{col 71}{space 3} 6.171255
{txt}           /cut4 {c |}{col 18}{res}{space 2} 4.955536{col 30}{space 2}  1.27495{col 58}{space 4}  2.45668{col 71}{space 3} 7.454392
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-824.51523}  
Iteration 1:{space 3}log pseudolikelihood = {res:-744.13273}  
Iteration 2:{space 3}log pseudolikelihood = {res:-741.69855}  
Iteration 3:{space 3}log pseudolikelihood = {res:-741.69051}  
Iteration 4:{space 3}log pseudolikelihood = {res:-741.69051}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       688
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     92.18
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-741.69051{txt}{col 51}Pseudo R2{col 67}= {res}    0.1005

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}    in_dislike_w11{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      z{col 52}   P>|z|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
disagree_total_int {c |}{col 20}{res}{space 2} .0266375{col 32}{space 2} .0136616{col 43}{space 1}    1.95{col 52}{space 3}0.051{col 60}{space 4}-.0001388{col 73}{space 3} .0534138
{txt}{space 9}numgiven1 {c |}{col 20}{res}{space 2}-.5277241{col 32}{space 2} .2600106{col 43}{space 1}   -2.03{col 52}{space 3}0.042{col 60}{space 4}-1.037336{col 73}{space 3}-.0181127
{txt}{space 2}network_interest {c |}{col 20}{res}{space 2}  .003083{col 32}{space 2}  .152478{col 43}{space 1}    0.02{col 52}{space 3}0.984{col 60}{space 4}-.2957683{col 73}{space 3} .3019344
{txt}{space 18} {c |}
{space 12}pid_21 {c |}
{space 9}Democrat  {c |}{col 20}{res}{space 2}-.8290079{col 32}{space 2}  .231035{col 43}{space 1}   -3.59{col 52}{space 3}0.000{col 60}{space 4}-1.281828{col 73}{space 3}-.3761877
{txt}{space 7}interest_w1 {c |}{col 20}{res}{space 2} .2916487{col 32}{space 2} .1371728{col 43}{space 1}    2.13{col 52}{space 3}0.033{col 60}{space 4}  .022795{col 73}{space 3} .5605024
{txt}{space 18} {c |}
{space 12}gender {c |}
{space 11}Female  {c |}{col 20}{res}{space 2}-.2856259{col 32}{space 2} .2401334{col 43}{space 1}   -1.19{col 52}{space 3}0.234{col 60}{space 4}-.7562788{col 73}{space 3}  .185027
{txt}{space 18} {c |}
{space 10}race_eth {c |}
{space 12}Black  {c |}{col 20}{res}{space 2}-1.123753{col 32}{space 2}  .513543{col 43}{space 1}   -2.19{col 52}{space 3}0.029{col 60}{space 4}-2.130278{col 73}{space 3}-.1172268
{txt}{space 9}Hispanic  {c |}{col 20}{res}{space 2}-.3575482{col 32}{space 2} .4937514{col 43}{space 1}   -0.72{col 52}{space 3}0.469{col 60}{space 4}-1.325283{col 73}{space 3} .6101867
{txt}{space 12}Other  {c |}{col 20}{res}{space 2} .9064577{col 32}{space 2} .4841429{col 43}{space 1}    1.87{col 52}{space 3}0.061{col 60}{space 4} -.042445{col 73}{space 3}  1.85536
{txt}{space 18} {c |}
{space 15}age {c |}{col 20}{res}{space 2} .0192131{col 32}{space 2} .0079432{col 43}{space 1}    2.42{col 52}{space 3}0.016{col 60}{space 4} .0036447{col 73}{space 3} .0347814
{txt}{space 14}educ {c |}{col 20}{res}{space 2} .1640785{col 32}{space 2} .1488872{col 43}{space 1}    1.10{col 52}{space 3}0.270{col 60}{space 4}-.1277351{col 73}{space 3}  .455892
{txt}{space 12}income {c |}{col 20}{res}{space 2}  .031918{col 32}{space 2} .0322291{col 43}{space 1}    0.99{col 52}{space 3}0.322{col 60}{space 4}  -.03125{col 73}{space 3} .0950859
{txt}{space 18} {c |}
{space 11}marital {c |}
{space 10}Married  {c |}{col 20}{res}{space 2}-.4878929{col 32}{space 2} .2486743{col 43}{space 1}   -1.96{col 52}{space 3}0.050{col 60}{space 4}-.9752855{col 73}{space 3}-.0005003
{txt}{space 9}evaluate1 {c |}{col 20}{res}{space 2} .6935778{col 32}{space 2} .3155783{col 43}{space 1}    2.20{col 52}{space 3}0.028{col 60}{space 4} .0750556{col 73}{space 3}   1.3121
{txt}{space 14}nfc1 {c |}{col 20}{res}{space 2} .0764554{col 32}{space 2} .1630295{col 43}{space 1}    0.47{col 52}{space 3}0.639{col 60}{space 4}-.2430766{col 73}{space 3} .3959875
{txt}{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
             /cut1 {c |}{col 20}{res}{space 2} 1.056361{col 32}{space 2} 1.078342{col 60}{space 4} -1.05715{col 73}{space 3} 3.169872
{txt}             /cut2 {c |}{col 20}{res}{space 2} 2.164007{col 32}{space 2} 1.102528{col 60}{space 4} .0030927{col 73}{space 3} 4.324921
{txt}             /cut3 {c |}{col 20}{res}{space 2}  4.07336{col 32}{space 2} 1.165602{col 60}{space 4} 1.788821{col 73}{space 3} 6.357898
{txt}             /cut4 {c |}{col 20}{res}{space 2} 5.085834{col 32}{space 2} 1.302235{col 60}{space 4} 2.533501{col 73}{space 3} 7.638167
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-824.51523}  
Iteration 1:{space 3}log pseudolikelihood = {res:-744.45571}  
Iteration 2:{space 3}log pseudolikelihood = {res:-742.01656}  
Iteration 3:{space 3}log pseudolikelihood = {res:-742.00858}  
Iteration 4:{space 3}log pseudolikelihood = {res:-742.00858}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       688
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     90.73
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-742.00858{txt}{col 51}Pseudo R2{col 67}= {res}    0.1001

{txt}{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}      in_dislike_w11{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
disagree_total_close {c |}{col 22}{res}{space 2} .0231859{col 34}{space 2} .0125868{col 45}{space 1}    1.84{col 54}{space 3}0.065{col 62}{space 4}-.0014838{col 75}{space 3} .0478556
{txt}{space 11}numgiven1 {c |}{col 22}{res}{space 2}-.5367046{col 34}{space 2} .2604124{col 45}{space 1}   -2.06{col 54}{space 3}0.039{col 62}{space 4}-1.047104{col 75}{space 3}-.0263056
{txt}{space 4}network_interest {c |}{col 22}{res}{space 2}-.0202213{col 34}{space 2} .1495416{col 45}{space 1}   -0.14{col 54}{space 3}0.892{col 62}{space 4}-.3133174{col 75}{space 3} .2728747
{txt}{space 20} {c |}
{space 14}pid_21 {c |}
{space 11}Democrat  {c |}{col 22}{res}{space 2}-.8373948{col 34}{space 2} .2342235{col 45}{space 1}   -3.58{col 54}{space 3}0.000{col 62}{space 4}-1.296464{col 75}{space 3}-.3783251
{txt}{space 9}interest_w1 {c |}{col 22}{res}{space 2} .2828961{col 34}{space 2} .1368075{col 45}{space 1}    2.07{col 54}{space 3}0.039{col 62}{space 4} .0147584{col 75}{space 3} .5510338
{txt}{space 20} {c |}
{space 14}gender {c |}
{space 13}Female  {c |}{col 22}{res}{space 2}-.2843349{col 34}{space 2} .2387641{col 45}{space 1}   -1.19{col 54}{space 3}0.234{col 62}{space 4} -.752304{col 75}{space 3} .1836341
{txt}{space 20} {c |}
{space 12}race_eth {c |}
{space 14}Black  {c |}{col 22}{res}{space 2}-1.129891{col 34}{space 2} .5162737{col 45}{space 1}   -2.19{col 54}{space 3}0.029{col 62}{space 4}-2.141769{col 75}{space 3}-.1180133
{txt}{space 11}Hispanic  {c |}{col 22}{res}{space 2}-.3741285{col 34}{space 2} .4938764{col 45}{space 1}   -0.76{col 54}{space 3}0.449{col 62}{space 4}-1.342108{col 75}{space 3} .5938514
{txt}{space 14}Other  {c |}{col 22}{res}{space 2} .9075363{col 34}{space 2} .4899408{col 45}{space 1}    1.85{col 54}{space 3}0.064{col 62}{space 4}-.0527299{col 75}{space 3} 1.867803
{txt}{space 20} {c |}
{space 17}age {c |}{col 22}{res}{space 2} .0195522{col 34}{space 2} .0080101{col 45}{space 1}    2.44{col 54}{space 3}0.015{col 62}{space 4} .0038526{col 75}{space 3} .0352517
{txt}{space 16}educ {c |}{col 22}{res}{space 2} .1661992{col 34}{space 2} .1474696{col 45}{space 1}    1.13{col 54}{space 3}0.260{col 62}{space 4}-.1228358{col 75}{space 3} .4552342
{txt}{space 14}income {c |}{col 22}{res}{space 2} .0325987{col 34}{space 2} .0321888{col 45}{space 1}    1.01{col 54}{space 3}0.311{col 62}{space 4}-.0304903{col 75}{space 3} .0956877
{txt}{space 20} {c |}
{space 13}marital {c |}
{space 12}Married  {c |}{col 22}{res}{space 2}-.4838753{col 34}{space 2} .2491916{col 45}{space 1}   -1.94{col 54}{space 3}0.052{col 62}{space 4}-.9722818{col 75}{space 3} .0045312
{txt}{space 11}evaluate1 {c |}{col 22}{res}{space 2} .6959694{col 34}{space 2} .3144809{col 45}{space 1}    2.21{col 54}{space 3}0.027{col 62}{space 4} .0795982{col 75}{space 3} 1.312341
{txt}{space 16}nfc1 {c |}{col 22}{res}{space 2} .0829368{col 34}{space 2} .1619949{col 45}{space 1}    0.51{col 54}{space 3}0.609{col 62}{space 4}-.2345672{col 75}{space 3} .4004409
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
               /cut1 {c |}{col 22}{res}{space 2} .9576558{col 34}{space 2} 1.058822{col 62}{space 4}-1.117598{col 75}{space 3} 3.032909
{txt}               /cut2 {c |}{col 22}{res}{space 2}  2.06375{col 34}{space 2} 1.082022{col 62}{space 4}-.0569741{col 75}{space 3} 4.184475
{txt}               /cut3 {c |}{col 22}{res}{space 2} 3.972514{col 34}{space 2} 1.141538{col 62}{space 4} 1.735141{col 75}{space 3} 6.209887
{txt}               /cut4 {c |}{col 22}{res}{space 2} 4.985854{col 34}{space 2} 1.275809{col 62}{space 4} 2.485314{col 75}{space 3} 7.486395
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-824.51523}  
Iteration 1:{space 3}log pseudolikelihood = {res:-743.25471}  
Iteration 2:{space 3}log pseudolikelihood = {res:-740.77493}  
Iteration 3:{space 3}log pseudolikelihood = {res:-740.76656}  
Iteration 4:{space 3}log pseudolikelihood = {res:-740.76656}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       688
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     95.11
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-740.76656{txt}{col 51}Pseudo R2{col 67}= {res}    0.1016

{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}    Robust
{col 1}       in_dislike_w11{col 23}{c |}      Coef.{col 35}   Std. Err.{col 47}      z{col 55}   P>|z|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
disagree_total_weight {c |}{col 23}{res}{space 2} .0330885{col 35}{space 2} .0145077{col 46}{space 1}    2.28{col 55}{space 3}0.023{col 63}{space 4} .0046539{col 76}{space 3} .0615231
{txt}{space 12}numgiven1 {c |}{col 23}{res}{space 2}-.5092635{col 35}{space 2} .2579051{col 46}{space 1}   -1.97{col 55}{space 3}0.048{col 63}{space 4}-1.014748{col 76}{space 3}-.0037789
{txt}{space 5}network_interest {c |}{col 23}{res}{space 2} -.011716{col 35}{space 2} .1513861{col 46}{space 1}   -0.08{col 55}{space 3}0.938{col 63}{space 4}-.3084272{col 76}{space 3} .2849953
{txt}{space 21} {c |}
{space 15}pid_21 {c |}
{space 12}Democrat  {c |}{col 23}{res}{space 2}-.8464087{col 35}{space 2} .2322417{col 46}{space 1}   -3.64{col 55}{space 3}0.000{col 63}{space 4}-1.301594{col 76}{space 3}-.3912234
{txt}{space 10}interest_w1 {c |}{col 23}{res}{space 2} .2826613{col 35}{space 2} .1375399{col 46}{space 1}    2.06{col 55}{space 3}0.040{col 63}{space 4} .0130881{col 76}{space 3} .5522346
{txt}{space 21} {c |}
{space 15}gender {c |}
{space 14}Female  {c |}{col 23}{res}{space 2}-.2826585{col 35}{space 2} .2401313{col 46}{space 1}   -1.18{col 55}{space 3}0.239{col 63}{space 4}-.7533071{col 76}{space 3} .1879901
{txt}{space 21} {c |}
{space 13}race_eth {c |}
{space 15}Black  {c |}{col 23}{res}{space 2}-1.110607{col 35}{space 2} .5144119{col 46}{space 1}   -2.16{col 55}{space 3}0.031{col 63}{space 4}-2.118836{col 76}{space 3}-.1023782
{txt}{space 12}Hispanic  {c |}{col 23}{res}{space 2}-.3895187{col 35}{space 2} .5000282{col 46}{space 1}   -0.78{col 55}{space 3}0.436{col 63}{space 4}-1.369556{col 76}{space 3} .5905185
{txt}{space 15}Other  {c |}{col 23}{res}{space 2} .8977212{col 35}{space 2} .4812182{col 46}{space 1}    1.87{col 55}{space 3}0.062{col 63}{space 4}-.0454492{col 76}{space 3} 1.840891
{txt}{space 21} {c |}
{space 18}age {c |}{col 23}{res}{space 2} .0198921{col 35}{space 2} .0080571{col 46}{space 1}    2.47{col 55}{space 3}0.014{col 63}{space 4} .0041004{col 76}{space 3} .0356838
{txt}{space 17}educ {c |}{col 23}{res}{space 2} .1582535{col 35}{space 2}  .149635{col 46}{space 1}    1.06{col 55}{space 3}0.290{col 63}{space 4}-.1350257{col 76}{space 3} .4515328
{txt}{space 15}income {c |}{col 23}{res}{space 2}    .0327{col 35}{space 2} .0323295{col 46}{space 1}    1.01{col 55}{space 3}0.312{col 63}{space 4}-.0306647{col 76}{space 3} .0960648
{txt}{space 21} {c |}
{space 14}marital {c |}
{space 13}Married  {c |}{col 23}{res}{space 2}-.4922837{col 35}{space 2} .2477884{col 46}{space 1}   -1.99{col 55}{space 3}0.047{col 63}{space 4}-.9779401{col 76}{space 3}-.0066273
{txt}{space 12}evaluate1 {c |}{col 23}{res}{space 2} .7083142{col 35}{space 2} .3135264{col 46}{space 1}    2.26{col 55}{space 3}0.024{col 63}{space 4} .0938137{col 76}{space 3} 1.322815
{txt}{space 17}nfc1 {c |}{col 23}{res}{space 2}  .072198{col 35}{space 2} .1644691{col 46}{space 1}    0.44{col 55}{space 3}0.661{col 63}{space 4}-.2501556{col 76}{space 3} .3945516
{txt}{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                /cut1 {c |}{col 23}{res}{space 2} .9763035{col 35}{space 2} 1.050355{col 63}{space 4}-1.082355{col 76}{space 3} 3.034962
{txt}                /cut2 {c |}{col 23}{res}{space 2} 2.085004{col 35}{space 2} 1.073139{col 63}{space 4} -.018311{col 76}{space 3} 4.188318
{txt}                /cut3 {c |}{col 23}{res}{space 2} 4.000636{col 35}{space 2} 1.131849{col 63}{space 4} 1.782253{col 76}{space 3} 6.219019
{txt}                /cut4 {c |}{col 23}{res}{space 2}  5.01591{col 35}{space 2} 1.269098{col 63}{space 4} 2.528524{col 76}{space 3} 7.503297
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est5{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-824.90784}  
Iteration 1:{space 3}log pseudolikelihood = {res:-742.43841}  
Iteration 2:{space 3}log pseudolikelihood = {res:-739.53541}  
Iteration 3:{space 3}log pseudolikelihood = {res:-739.52499}  
Iteration 4:{space 3}log pseudolikelihood = {res:-739.52499}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       689
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}    104.02
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-739.52499{txt}{col 51}Pseudo R2{col 67}= {res}    0.1035

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}  in_dislike_w11{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}gendiff {c |}{col 18}{res}{space 2}  .339121{col 30}{space 2} .1747066{col 41}{space 1}    1.94{col 50}{space 3}0.052{col 58}{space 4}-.0032977{col 71}{space 3} .6815398
{txt}{space 7}numgiven1 {c |}{col 18}{res}{space 2} -.624399{col 30}{space 2} .2518363{col 41}{space 1}   -2.48{col 50}{space 3}0.013{col 58}{space 4}-1.117989{col 71}{space 3} -.130809
{txt}network_interest {c |}{col 18}{res}{space 2} .0028337{col 30}{space 2} .1486799{col 41}{space 1}    0.02{col 50}{space 3}0.985{col 58}{space 4}-.2885737{col 71}{space 3}  .294241
{txt}{space 16} {c |}
{space 10}pid_21 {c |}
{space 7}Democrat  {c |}{col 18}{res}{space 2}-.8557315{col 30}{space 2}  .224901{col 41}{space 1}   -3.80{col 50}{space 3}0.000{col 58}{space 4}-1.296529{col 71}{space 3}-.4149337
{txt}{space 5}interest_w1 {c |}{col 18}{res}{space 2} .2620583{col 30}{space 2} .1380207{col 41}{space 1}    1.90{col 50}{space 3}0.058{col 58}{space 4}-.0084573{col 71}{space 3} .5325738
{txt}{space 16} {c |}
{space 10}gender {c |}
{space 9}Female  {c |}{col 18}{res}{space 2}-.3272754{col 30}{space 2} .2416188{col 41}{space 1}   -1.35{col 50}{space 3}0.176{col 58}{space 4}-.8008396{col 71}{space 3} .1462888
{txt}{space 16} {c |}
{space 8}race_eth {c |}
{space 10}Black  {c |}{col 18}{res}{space 2}-1.120471{col 30}{space 2} .5149188{col 41}{space 1}   -2.18{col 50}{space 3}0.030{col 58}{space 4}-2.129693{col 71}{space 3}-.1112482
{txt}{space 7}Hispanic  {c |}{col 18}{res}{space 2}-.3787785{col 30}{space 2}  .477167{col 41}{space 1}   -0.79{col 50}{space 3}0.427{col 58}{space 4}-1.314009{col 71}{space 3} .5564516
{txt}{space 10}Other  {c |}{col 18}{res}{space 2} .9499961{col 30}{space 2} .4479952{col 41}{space 1}    2.12{col 50}{space 3}0.034{col 58}{space 4} .0719416{col 71}{space 3} 1.828051
{txt}{space 16} {c |}
{space 13}age {c |}{col 18}{res}{space 2} .0219325{col 30}{space 2} .0083641{col 41}{space 1}    2.62{col 50}{space 3}0.009{col 58}{space 4} .0055391{col 71}{space 3} .0383258
{txt}{space 12}educ {c |}{col 18}{res}{space 2} .1489968{col 30}{space 2} .1501172{col 41}{space 1}    0.99{col 50}{space 3}0.321{col 58}{space 4}-.1452276{col 71}{space 3} .4432212
{txt}{space 10}income {c |}{col 18}{res}{space 2} .0338178{col 30}{space 2} .0312468{col 41}{space 1}    1.08{col 50}{space 3}0.279{col 58}{space 4}-.0274248{col 71}{space 3} .0950604
{txt}{space 16} {c |}
{space 9}marital {c |}
{space 8}Married  {c |}{col 18}{res}{space 2}-.5006963{col 30}{space 2} .2439218{col 41}{space 1}   -2.05{col 50}{space 3}0.040{col 58}{space 4}-.9787743{col 71}{space 3}-.0226182
{txt}{space 7}evaluate1 {c |}{col 18}{res}{space 2} .7048805{col 30}{space 2} .3202748{col 41}{space 1}    2.20{col 50}{space 3}0.028{col 58}{space 4} .0771534{col 71}{space 3} 1.332608
{txt}{space 12}nfc1 {c |}{col 18}{res}{space 2}  .052998{col 30}{space 2} .1720784{col 41}{space 1}    0.31{col 50}{space 3}0.758{col 58}{space 4}-.2842694{col 71}{space 3} .3902654
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
           /cut1 {c |}{col 18}{res}{space 2} 1.573563{col 30}{space 2} .9559077{col 58}{space 4}-.2999813{col 71}{space 3} 3.447108
{txt}           /cut2 {c |}{col 18}{res}{space 2} 2.682732{col 30}{space 2} .9784922{col 58}{space 4} .7649223{col 71}{space 3} 4.600541
{txt}           /cut3 {c |}{col 18}{res}{space 2} 4.610097{col 30}{space 2} 1.016196{col 58}{space 4} 2.618388{col 71}{space 3} 6.601805
{txt}           /cut4 {c |}{col 18}{res}{space 2} 5.636243{col 30}{space 2} 1.151334{col 58}{space 4}  3.37967{col 71}{space 3} 7.892816
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est6{txt} stored)

{com}. 
. 
. esttab using 2008AMBIV_ALTMEASURES_INDISLIKE.rtf, onecell nobaselevels replace label pr2 aic bic se star(+ 0.10 * 0.05 ** 0.01) ///
>         mtitles("Original Measure" "(/D+A)" "Weigh: by Disc. Interest" ///
>                 "Weigh: by Tie Strength" "Weighted by Gen Dis" "Gen Disagree")  ///
>         title({c -(}\b Table XX.{c )-} "Alternative Measures of Disagreement - 2008-2009 ANES (AMBIVALENCE: IN-DISLIKE)") ///
>         rename(disagree_total Disagreement disagree_avg "Disagreement" disagree_total_int Disagreement disagree_total_close Disagreement ///
>                                 disagree_total_weight Disagreement gendiff Disagreement) 
{res}{txt}(output written to {browse  `"2008AMBIV_ALTMEASURES_INDISLIKE.rtf"'})

{com}. 
. 
. *out like                       
. 
. eststo clear
{txt}
{com}. foreach var in disagree_total disagree_avg disagree_total_int disagree_total_close  ///
>         disagree_total_weight gendiff  {c -(}
{txt}  2{com}. eststo: ologit out_like_w11 `var'  numgiven1 network_interest  i.pid_21 interest_w1 i.gender ///
>                         i.race age educ income i.marital evaluate1 nfc1 [pweight= WGTC11]
{txt}  3{com}.         {c )-}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-711.38006}  
Iteration 1:{space 3}log pseudolikelihood = {res:-679.05066}  
Iteration 2:{space 3}log pseudolikelihood = {res:-678.20257}  
Iteration 3:{space 3}log pseudolikelihood = {res:-678.20039}  
Iteration 4:{space 3}log pseudolikelihood = {res:-678.20039}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       756
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     41.06
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0003
{txt}Log pseudolikelihood = {res}-678.20039{txt}{col 51}Pseudo R2{col 67}= {res}    0.0466

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}    out_like_w11{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}disagree_total {c |}{col 18}{res}{space 2} .1541668{col 30}{space 2} .0478952{col 41}{space 1}    3.22{col 50}{space 3}0.001{col 58}{space 4} .0602938{col 71}{space 3} .2480398
{txt}{space 7}numgiven1 {c |}{col 18}{res}{space 2} .5169671{col 30}{space 2} .2702706{col 41}{space 1}    1.91{col 50}{space 3}0.056{col 58}{space 4}-.0127536{col 71}{space 3} 1.046688
{txt}network_interest {c |}{col 18}{res}{space 2}-.1076322{col 30}{space 2} .1460438{col 41}{space 1}   -0.74{col 50}{space 3}0.461{col 58}{space 4}-.3938729{col 71}{space 3} .1786084
{txt}{space 16} {c |}
{space 10}pid_21 {c |}
{space 7}Democrat  {c |}{col 18}{res}{space 2}-.1214715{col 30}{space 2} .2284983{col 41}{space 1}   -0.53{col 50}{space 3}0.595{col 58}{space 4}  -.56932{col 71}{space 3}  .326377
{txt}{space 5}interest_w1 {c |}{col 18}{res}{space 2} -.106017{col 30}{space 2} .1112558{col 41}{space 1}   -0.95{col 50}{space 3}0.341{col 58}{space 4}-.3240745{col 71}{space 3} .1120404
{txt}{space 16} {c |}
{space 10}gender {c |}
{space 9}Female  {c |}{col 18}{res}{space 2} .4876268{col 30}{space 2} .2233005{col 41}{space 1}    2.18{col 50}{space 3}0.029{col 58}{space 4} .0499659{col 71}{space 3} .9252877
{txt}{space 16} {c |}
{space 8}race_eth {c |}
{space 10}Black  {c |}{col 18}{res}{space 2}-1.423019{col 30}{space 2} .4854683{col 41}{space 1}   -2.93{col 50}{space 3}0.003{col 58}{space 4} -2.37452{col 71}{space 3}-.4715191
{txt}{space 7}Hispanic  {c |}{col 18}{res}{space 2}-.1416363{col 30}{space 2} .4005813{col 41}{space 1}   -0.35{col 50}{space 3}0.724{col 58}{space 4}-.9267613{col 71}{space 3} .6434886
{txt}{space 10}Other  {c |}{col 18}{res}{space 2}-1.617336{col 30}{space 2} .7341269{col 41}{space 1}   -2.20{col 50}{space 3}0.028{col 58}{space 4}-3.056198{col 71}{space 3}-.1784734
{txt}{space 16} {c |}
{space 13}age {c |}{col 18}{res}{space 2}  .010052{col 30}{space 2} .0074546{col 41}{space 1}    1.35{col 50}{space 3}0.178{col 58}{space 4}-.0045588{col 71}{space 3} .0246628
{txt}{space 12}educ {c |}{col 18}{res}{space 2} -.063397{col 30}{space 2} .1080842{col 41}{space 1}   -0.59{col 50}{space 3}0.558{col 58}{space 4}-.2752381{col 71}{space 3} .1484442
{txt}{space 10}income {c |}{col 18}{res}{space 2} .0298219{col 30}{space 2} .0274062{col 41}{space 1}    1.09{col 50}{space 3}0.277{col 58}{space 4}-.0238932{col 71}{space 3}  .083537
{txt}{space 16} {c |}
{space 9}marital {c |}
{space 8}Married  {c |}{col 18}{res}{space 2}-.0745925{col 30}{space 2} .2562185{col 41}{space 1}   -0.29{col 50}{space 3}0.771{col 58}{space 4}-.5767716{col 71}{space 3} .4275865
{txt}{space 7}evaluate1 {c |}{col 18}{res}{space 2} .2780788{col 30}{space 2} .2804419{col 41}{space 1}    0.99{col 50}{space 3}0.321{col 58}{space 4}-.2715774{col 71}{space 3} .8277349
{txt}{space 12}nfc1 {c |}{col 18}{res}{space 2}-.0035591{col 30}{space 2} .1658227{col 41}{space 1}   -0.02{col 50}{space 3}0.983{col 58}{space 4}-.3285657{col 71}{space 3} .3214475
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
           /cut1 {c |}{col 18}{res}{space 2} 1.952099{col 30}{space 2} .9395627{col 58}{space 4} .1105896{col 71}{space 3} 3.793608
{txt}           /cut2 {c |}{col 18}{res}{space 2} 3.038048{col 30}{space 2} .9523828{col 58}{space 4} 1.171412{col 71}{space 3} 4.904684
{txt}           /cut3 {c |}{col 18}{res}{space 2} 5.446075{col 30}{space 2} .9817207{col 58}{space 4} 3.521937{col 71}{space 3} 7.370212
{txt}           /cut4 {c |}{col 18}{res}{space 2} 7.652449{col 30}{space 2} 1.129279{col 58}{space 4} 5.439103{col 71}{space 3} 9.865795
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-711.38006}  
Iteration 1:{space 3}log pseudolikelihood = {res:-679.94495}  
Iteration 2:{space 3}log pseudolikelihood = {res:-679.19904}  
Iteration 3:{space 3}log pseudolikelihood = {res:-679.19709}  
Iteration 4:{space 3}log pseudolikelihood = {res:-679.19709}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       756
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     37.04
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0013
{txt}Log pseudolikelihood = {res}-679.19709{txt}{col 51}Pseudo R2{col 67}= {res}    0.0452

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}    out_like_w11{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}disagree_avg {c |}{col 18}{res}{space 2} .3992639{col 30}{space 2} .1432285{col 41}{space 1}    2.79{col 50}{space 3}0.005{col 58}{space 4} .1185412{col 71}{space 3} .6799866
{txt}{space 7}numgiven1 {c |}{col 18}{res}{space 2} .5103121{col 30}{space 2} .2860805{col 41}{space 1}    1.78{col 50}{space 3}0.074{col 58}{space 4}-.0503953{col 71}{space 3}  1.07102
{txt}network_interest {c |}{col 18}{res}{space 2}-.1041277{col 30}{space 2} .1468445{col 41}{space 1}   -0.71{col 50}{space 3}0.478{col 58}{space 4}-.3919377{col 71}{space 3} .1836822
{txt}{space 16} {c |}
{space 10}pid_21 {c |}
{space 7}Democrat  {c |}{col 18}{res}{space 2}-.1091938{col 30}{space 2} .2295399{col 41}{space 1}   -0.48{col 50}{space 3}0.634{col 58}{space 4}-.5590839{col 71}{space 3} .3406962
{txt}{space 5}interest_w1 {c |}{col 18}{res}{space 2}-.1100324{col 30}{space 2} .1115829{col 41}{space 1}   -0.99{col 50}{space 3}0.324{col 58}{space 4}-.3287309{col 71}{space 3} .1086661
{txt}{space 16} {c |}
{space 10}gender {c |}
{space 9}Female  {c |}{col 18}{res}{space 2} .4773464{col 30}{space 2} .2214911{col 41}{space 1}    2.16{col 50}{space 3}0.031{col 58}{space 4} .0432318{col 71}{space 3}  .911461
{txt}{space 16} {c |}
{space 8}race_eth {c |}
{space 10}Black  {c |}{col 18}{res}{space 2}-1.430906{col 30}{space 2} .4822948{col 41}{space 1}   -2.97{col 50}{space 3}0.003{col 58}{space 4}-2.376186{col 71}{space 3}-.4856255
{txt}{space 7}Hispanic  {c |}{col 18}{res}{space 2}-.1395524{col 30}{space 2} .4011833{col 41}{space 1}   -0.35{col 50}{space 3}0.728{col 58}{space 4}-.9258571{col 71}{space 3} .6467524
{txt}{space 10}Other  {c |}{col 18}{res}{space 2}-1.592462{col 30}{space 2} .7274951{col 41}{space 1}   -2.19{col 50}{space 3}0.029{col 58}{space 4}-3.018326{col 71}{space 3}-.1665976
{txt}{space 16} {c |}
{space 13}age {c |}{col 18}{res}{space 2} .0100377{col 30}{space 2} .0074514{col 41}{space 1}    1.35{col 50}{space 3}0.178{col 58}{space 4}-.0045668{col 71}{space 3} .0246421
{txt}{space 12}educ {c |}{col 18}{res}{space 2}-.0667242{col 30}{space 2} .1079921{col 41}{space 1}   -0.62{col 50}{space 3}0.537{col 58}{space 4}-.2783848{col 71}{space 3} .1449365
{txt}{space 10}income {c |}{col 18}{res}{space 2}  .030574{col 30}{space 2} .0274552{col 41}{space 1}    1.11{col 50}{space 3}0.265{col 58}{space 4}-.0232372{col 71}{space 3} .0843852
{txt}{space 16} {c |}
{space 9}marital {c |}
{space 8}Married  {c |}{col 18}{res}{space 2}-.0820627{col 30}{space 2} .2567029{col 41}{space 1}   -0.32{col 50}{space 3}0.749{col 58}{space 4}-.5851912{col 71}{space 3} .4210658
{txt}{space 7}evaluate1 {c |}{col 18}{res}{space 2} .2699489{col 30}{space 2} .2790106{col 41}{space 1}    0.97{col 50}{space 3}0.333{col 58}{space 4}-.2769019{col 71}{space 3} .8167997
{txt}{space 12}nfc1 {c |}{col 18}{res}{space 2}-.0011546{col 30}{space 2} .1651346{col 41}{space 1}   -0.01{col 50}{space 3}0.994{col 58}{space 4}-.3248125{col 71}{space 3} .3225033
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
           /cut1 {c |}{col 18}{res}{space 2} 1.935057{col 30}{space 2} .9808268{col 58}{space 4} .0126717{col 71}{space 3} 3.857442
{txt}           /cut2 {c |}{col 18}{res}{space 2} 3.017699{col 30}{space 2} .9956006{col 58}{space 4} 1.066358{col 71}{space 3} 4.969041
{txt}           /cut3 {c |}{col 18}{res}{space 2} 5.422335{col 30}{space 2} 1.022298{col 58}{space 4} 3.418667{col 71}{space 3} 7.426002
{txt}           /cut4 {c |}{col 18}{res}{space 2} 7.628531{col 30}{space 2} 1.165043{col 58}{space 4} 5.345088{col 71}{space 3} 9.911973
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-711.38006}  
Iteration 1:{space 3}log pseudolikelihood = {res:-676.80337}  
Iteration 2:{space 3}log pseudolikelihood = {res:-675.82031}  
Iteration 3:{space 3}log pseudolikelihood = {res:-675.81779}  
Iteration 4:{space 3}log pseudolikelihood = {res:-675.81779}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       756
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     48.66
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-675.81779{txt}{col 51}Pseudo R2{col 67}= {res}    0.0500

{txt}{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}      out_like_w11{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      z{col 52}   P>|z|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
disagree_total_int {c |}{col 20}{res}{space 2} .0470237{col 32}{space 2} .0122218{col 43}{space 1}    3.85{col 52}{space 3}0.000{col 60}{space 4} .0230694{col 73}{space 3} .0709779
{txt}{space 9}numgiven1 {c |}{col 20}{res}{space 2} .5297913{col 32}{space 2} .2690336{col 43}{space 1}    1.97{col 52}{space 3}0.049{col 60}{space 4} .0024951{col 73}{space 3} 1.057087
{txt}{space 2}network_interest {c |}{col 20}{res}{space 2}-.0657124{col 32}{space 2} .1455408{col 43}{space 1}   -0.45{col 52}{space 3}0.652{col 60}{space 4}-.3509671{col 73}{space 3} .2195424
{txt}{space 18} {c |}
{space 12}pid_21 {c |}
{space 9}Democrat  {c |}{col 20}{res}{space 2}-.1179808{col 32}{space 2} .2284464{col 43}{space 1}   -0.52{col 52}{space 3}0.606{col 60}{space 4}-.5657276{col 73}{space 3}  .329766
{txt}{space 7}interest_w1 {c |}{col 20}{res}{space 2}-.0974296{col 32}{space 2} .1115049{col 43}{space 1}   -0.87{col 52}{space 3}0.382{col 60}{space 4}-.3159752{col 73}{space 3}  .121116
{txt}{space 18} {c |}
{space 12}gender {c |}
{space 11}Female  {c |}{col 20}{res}{space 2} .4909949{col 32}{space 2} .2226411{col 43}{space 1}    2.21{col 52}{space 3}0.027{col 60}{space 4} .0546265{col 73}{space 3} .9273633
{txt}{space 18} {c |}
{space 10}race_eth {c |}
{space 12}Black  {c |}{col 20}{res}{space 2}-1.420857{col 32}{space 2} .4881323{col 43}{space 1}   -2.91{col 52}{space 3}0.004{col 60}{space 4}-2.377579{col 73}{space 3}-.4641351
{txt}{space 9}Hispanic  {c |}{col 20}{res}{space 2}-.1553474{col 32}{space 2} .4008832{col 43}{space 1}   -0.39{col 52}{space 3}0.698{col 60}{space 4}-.9410641{col 73}{space 3} .6303692
{txt}{space 12}Other  {c |}{col 20}{res}{space 2}-1.620212{col 32}{space 2} .7286131{col 43}{space 1}   -2.22{col 52}{space 3}0.026{col 60}{space 4}-3.048267{col 73}{space 3}-.1921563
{txt}{space 18} {c |}
{space 15}age {c |}{col 20}{res}{space 2} .0088445{col 32}{space 2}  .007538{col 43}{space 1}    1.17{col 52}{space 3}0.241{col 60}{space 4}-.0059298{col 73}{space 3} .0236187
{txt}{space 14}educ {c |}{col 20}{res}{space 2}-.0556298{col 32}{space 2} .1086959{col 43}{space 1}   -0.51{col 52}{space 3}0.609{col 60}{space 4}-.2686698{col 73}{space 3} .1574101
{txt}{space 12}income {c |}{col 20}{res}{space 2} .0260198{col 32}{space 2} .0272548{col 43}{space 1}    0.95{col 52}{space 3}0.340{col 60}{space 4}-.0273987{col 73}{space 3} .0794383
{txt}{space 18} {c |}
{space 11}marital {c |}
{space 10}Married  {c |}{col 20}{res}{space 2} -.083091{col 32}{space 2} .2574484{col 43}{space 1}   -0.32{col 52}{space 3}0.747{col 60}{space 4}-.5876805{col 73}{space 3} .4214986
{txt}{space 9}evaluate1 {c |}{col 20}{res}{space 2}  .292034{col 32}{space 2}  .281411{col 43}{space 1}    1.04{col 52}{space 3}0.299{col 60}{space 4}-.2595214{col 73}{space 3} .8435894
{txt}{space 14}nfc1 {c |}{col 20}{res}{space 2} -.013248{col 32}{space 2} .1667196{col 43}{space 1}   -0.08{col 52}{space 3}0.937{col 60}{space 4}-.3400123{col 73}{space 3} .3135164
{txt}{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
             /cut1 {c |}{col 20}{res}{space 2} 2.062663{col 32}{space 2} .9331319{col 60}{space 4} .2337584{col 73}{space 3} 3.891568
{txt}             /cut2 {c |}{col 20}{res}{space 2} 3.154388{col 32}{space 2} .9449772{col 60}{space 4} 1.302267{col 73}{space 3} 5.006509
{txt}             /cut3 {c |}{col 20}{res}{space 2} 5.567783{col 32}{space 2} .9756037{col 60}{space 4} 3.655635{col 73}{space 3} 7.479931
{txt}             /cut4 {c |}{col 20}{res}{space 2}   7.7753{col 32}{space 2} 1.124863{col 60}{space 4}  5.57061{col 73}{space 3} 9.979991
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-711.38006}  
Iteration 1:{space 3}log pseudolikelihood = {res:-680.55236}  
Iteration 2:{space 3}log pseudolikelihood = {res:-679.74661}  
Iteration 3:{space 3}log pseudolikelihood = {res:-679.74455}  
Iteration 4:{space 3}log pseudolikelihood = {res:-679.74455}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       756
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     37.72
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0010
{txt}Log pseudolikelihood = {res}-679.74455{txt}{col 51}Pseudo R2{col 67}= {res}    0.0445

{txt}{hline 21}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 22}{c |}{col 34}    Robust
{col 1}        out_like_w11{col 22}{c |}      Coef.{col 34}   Std. Err.{col 46}      z{col 54}   P>|z|{col 62}     [95% Con{col 75}f. Interval]
{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
disagree_total_close {c |}{col 22}{res}{space 2} .0332094{col 34}{space 2} .0116694{col 45}{space 1}    2.85{col 54}{space 3}0.004{col 62}{space 4} .0103379{col 75}{space 3} .0560809
{txt}{space 11}numgiven1 {c |}{col 22}{res}{space 2} .5040437{col 34}{space 2}  .269379{col 45}{space 1}    1.87{col 54}{space 3}0.061{col 62}{space 4}-.0239294{col 75}{space 3} 1.032017
{txt}{space 4}network_interest {c |}{col 22}{res}{space 2}-.1077715{col 34}{space 2} .1445041{col 45}{space 1}   -0.75{col 54}{space 3}0.456{col 62}{space 4}-.3909944{col 75}{space 3} .1754514
{txt}{space 20} {c |}
{space 14}pid_21 {c |}
{space 11}Democrat  {c |}{col 22}{res}{space 2}-.1181844{col 34}{space 2} .2295048{col 45}{space 1}   -0.51{col 54}{space 3}0.607{col 62}{space 4}-.5680055{col 75}{space 3} .3316367
{txt}{space 9}interest_w1 {c |}{col 22}{res}{space 2}-.1149384{col 34}{space 2} .1120861{col 45}{space 1}   -1.03{col 54}{space 3}0.305{col 62}{space 4}-.3346231{col 75}{space 3} .1047463
{txt}{space 20} {c |}
{space 14}gender {c |}
{space 13}Female  {c |}{col 22}{res}{space 2} .4844641{col 34}{space 2}  .224463{col 45}{space 1}    2.16{col 54}{space 3}0.031{col 62}{space 4} .0445247{col 75}{space 3} .9244035
{txt}{space 20} {c |}
{space 12}race_eth {c |}
{space 14}Black  {c |}{col 22}{res}{space 2}-1.426538{col 34}{space 2} .4869763{col 45}{space 1}   -2.93{col 54}{space 3}0.003{col 62}{space 4}-2.380994{col 75}{space 3}-.4720822
{txt}{space 11}Hispanic  {c |}{col 22}{res}{space 2}-.1465894{col 34}{space 2} .3923886{col 45}{space 1}   -0.37{col 54}{space 3}0.709{col 62}{space 4}-.9156569{col 75}{space 3} .6224781
{txt}{space 14}Other  {c |}{col 22}{res}{space 2}-1.579892{col 34}{space 2} .7296423{col 45}{space 1}   -2.17{col 54}{space 3}0.030{col 62}{space 4}-3.009964{col 75}{space 3} -.149819
{txt}{space 20} {c |}
{space 17}age {c |}{col 22}{res}{space 2} .0102611{col 34}{space 2} .0075081{col 45}{space 1}    1.37{col 54}{space 3}0.172{col 62}{space 4}-.0044545{col 75}{space 3} .0249768
{txt}{space 16}educ {c |}{col 22}{res}{space 2}-.0657203{col 34}{space 2} .1097645{col 45}{space 1}   -0.60{col 54}{space 3}0.549{col 62}{space 4}-.2808547{col 75}{space 3} .1494141
{txt}{space 14}income {c |}{col 22}{res}{space 2} .0291721{col 34}{space 2} .0274303{col 45}{space 1}    1.06{col 54}{space 3}0.288{col 62}{space 4}-.0245903{col 75}{space 3} .0829344
{txt}{space 20} {c |}
{space 13}marital {c |}
{space 12}Married  {c |}{col 22}{res}{space 2}-.0725427{col 34}{space 2} .2559916{col 45}{space 1}   -0.28{col 54}{space 3}0.777{col 62}{space 4} -.574277{col 75}{space 3} .4291916
{txt}{space 11}evaluate1 {c |}{col 22}{res}{space 2} .2755417{col 34}{space 2} .2820616{col 45}{space 1}    0.98{col 54}{space 3}0.329{col 62}{space 4}-.2772889{col 75}{space 3} .8283723
{txt}{space 16}nfc1 {c |}{col 22}{res}{space 2} .0026462{col 34}{space 2} .1664889{col 45}{space 1}    0.02{col 54}{space 3}0.987{col 62}{space 4}-.3236661{col 75}{space 3} .3289584
{txt}{hline 21}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
               /cut1 {c |}{col 22}{res}{space 2} 1.890815{col 34}{space 2}  .935777{col 62}{space 4} .0567262{col 75}{space 3} 3.724905
{txt}               /cut2 {c |}{col 22}{res}{space 2} 2.973372{col 34}{space 2}  .947203{col 62}{space 4} 1.116889{col 75}{space 3} 4.829856
{txt}               /cut3 {c |}{col 22}{res}{space 2} 5.379583{col 34}{space 2} .9762294{col 62}{space 4} 3.466209{col 75}{space 3} 7.292958
{txt}               /cut4 {c |}{col 22}{res}{space 2} 7.585958{col 34}{space 2} 1.125674{col 62}{space 4} 5.379678{col 75}{space 3} 9.792238
{txt}{hline 21}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-711.38006}  
Iteration 1:{space 3}log pseudolikelihood = {res:-675.14331}  
Iteration 2:{space 3}log pseudolikelihood = {res:-674.12652}  
Iteration 3:{space 3}log pseudolikelihood = {res:-674.12386}  
Iteration 4:{space 3}log pseudolikelihood = {res:-674.12386}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       756
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     47.43
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-674.12386{txt}{col 51}Pseudo R2{col 67}= {res}    0.0524

{txt}{hline 22}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 23}{c |}{col 35}    Robust
{col 1}         out_like_w11{col 23}{c |}      Coef.{col 35}   Std. Err.{col 47}      z{col 55}   P>|z|{col 63}     [95% Con{col 76}f. Interval]
{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
disagree_total_weight {c |}{col 23}{res}{space 2} .0552045{col 35}{space 2} .0138852{col 46}{space 1}    3.98{col 55}{space 3}0.000{col 63}{space 4}   .02799{col 76}{space 3} .0824189
{txt}{space 12}numgiven1 {c |}{col 23}{res}{space 2} .5639888{col 35}{space 2} .2651602{col 46}{space 1}    2.13{col 55}{space 3}0.033{col 63}{space 4} .0442845{col 76}{space 3} 1.083693
{txt}{space 5}network_interest {c |}{col 23}{res}{space 2}-.1009456{col 35}{space 2} .1473269{col 46}{space 1}   -0.69{col 55}{space 3}0.493{col 63}{space 4}-.3897011{col 76}{space 3} .1878098
{txt}{space 21} {c |}
{space 15}pid_21 {c |}
{space 12}Democrat  {c |}{col 23}{res}{space 2} -.133103{col 35}{space 2}  .228753{col 46}{space 1}   -0.58{col 55}{space 3}0.561{col 63}{space 4}-.5814506{col 76}{space 3} .3152445
{txt}{space 10}interest_w1 {c |}{col 23}{res}{space 2}-.1177913{col 35}{space 2} .1129097{col 46}{space 1}   -1.04{col 55}{space 3}0.297{col 63}{space 4}-.3390903{col 76}{space 3} .1035076
{txt}{space 21} {c |}
{space 15}gender {c |}
{space 14}Female  {c |}{col 23}{res}{space 2} .5094418{col 35}{space 2} .2243466{col 46}{space 1}    2.27{col 55}{space 3}0.023{col 63}{space 4} .0697305{col 76}{space 3} .9491531
{txt}{space 21} {c |}
{space 13}race_eth {c |}
{space 15}Black  {c |}{col 23}{res}{space 2}-1.404721{col 35}{space 2} .4848313{col 46}{space 1}   -2.90{col 55}{space 3}0.004{col 63}{space 4}-2.354973{col 76}{space 3}-.4544691
{txt}{space 12}Hispanic  {c |}{col 23}{res}{space 2}-.2059147{col 35}{space 2} .3976555{col 46}{space 1}   -0.52{col 55}{space 3}0.605{col 63}{space 4} -.985305{col 76}{space 3} .5734757
{txt}{space 15}Other  {c |}{col 23}{res}{space 2}-1.634467{col 35}{space 2}  .733899{col 46}{space 1}   -2.23{col 55}{space 3}0.026{col 63}{space 4}-3.072882{col 76}{space 3} -.196051
{txt}{space 21} {c |}
{space 18}age {c |}{col 23}{res}{space 2} .0102838{col 35}{space 2} .0076304{col 46}{space 1}    1.35{col 55}{space 3}0.178{col 63}{space 4}-.0046716{col 76}{space 3} .0252392
{txt}{space 17}educ {c |}{col 23}{res}{space 2}-.0690508{col 35}{space 2} .1087726{col 46}{space 1}   -0.63{col 55}{space 3}0.526{col 63}{space 4}-.2822411{col 76}{space 3} .1441396
{txt}{space 15}income {c |}{col 23}{res}{space 2} .0286171{col 35}{space 2} .0273216{col 46}{space 1}    1.05{col 55}{space 3}0.295{col 63}{space 4}-.0249324{col 76}{space 3} .0821665
{txt}{space 21} {c |}
{space 14}marital {c |}
{space 13}Married  {c |}{col 23}{res}{space 2}-.0904906{col 35}{space 2} .2580815{col 46}{space 1}   -0.35{col 55}{space 3}0.726{col 63}{space 4}-.5963211{col 76}{space 3} .4153398
{txt}{space 12}evaluate1 {c |}{col 23}{res}{space 2} .3173138{col 35}{space 2} .2847646{col 46}{space 1}    1.11{col 55}{space 3}0.265{col 63}{space 4}-.2408145{col 76}{space 3} .8754421
{txt}{space 17}nfc1 {c |}{col 23}{res}{space 2} -.014222{col 35}{space 2} .1681609{col 46}{space 1}   -0.08{col 55}{space 3}0.933{col 63}{space 4}-.3438112{col 76}{space 3} .3153672
{txt}{hline 22}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                /cut1 {c |}{col 23}{res}{space 2} 1.936511{col 35}{space 2} .9283948{col 63}{space 4} .1168903{col 76}{space 3} 3.756131
{txt}                /cut2 {c |}{col 23}{res}{space 2} 3.033225{col 35}{space 2} .9394924{col 63}{space 4} 1.191854{col 76}{space 3} 4.874596
{txt}                /cut3 {c |}{col 23}{res}{space 2} 5.453872{col 35}{space 2} .9703111{col 63}{space 4} 3.552097{col 76}{space 3} 7.355646
{txt}                /cut4 {c |}{col 23}{res}{space 2} 7.662217{col 35}{space 2} 1.119208{col 63}{space 4}  5.46861{col 76}{space 3} 9.855824
{txt}{hline 22}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est5{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-711.78791}  
Iteration 1:{space 3}log pseudolikelihood = {res:-670.94131}  
Iteration 2:{space 3}log pseudolikelihood = {res:-669.81196}  
Iteration 3:{space 3}log pseudolikelihood = {res:-669.80771}  
Iteration 4:{space 3}log pseudolikelihood = {res:-669.80771}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}       757
{txt}{col 51}Wald chi2({res}15{txt}){col 67}= {res}     49.21
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-669.80771{txt}{col 51}Pseudo R2{col 67}= {res}    0.0590

{txt}{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}    out_like_w11{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}gendiff {c |}{col 18}{res}{space 2} .5806615{col 30}{space 2}  .153062{col 41}{space 1}    3.79{col 50}{space 3}0.000{col 58}{space 4} .2806655{col 71}{space 3} .8806575
{txt}{space 7}numgiven1 {c |}{col 18}{res}{space 2} .4914202{col 30}{space 2} .2333754{col 41}{space 1}    2.11{col 50}{space 3}0.035{col 58}{space 4} .0340128{col 71}{space 3} .9488276
{txt}network_interest {c |}{col 18}{res}{space 2}-.0976246{col 30}{space 2} .1442714{col 41}{space 1}   -0.68{col 50}{space 3}0.499{col 58}{space 4}-.3803914{col 71}{space 3} .1851422
{txt}{space 16} {c |}
{space 10}pid_21 {c |}
{space 7}Democrat  {c |}{col 18}{res}{space 2}-.1415954{col 30}{space 2} .2282925{col 41}{space 1}   -0.62{col 50}{space 3}0.535{col 58}{space 4}-.5890405{col 71}{space 3} .3058497
{txt}{space 5}interest_w1 {c |}{col 18}{res}{space 2}-.1550986{col 30}{space 2} .1168517{col 41}{space 1}   -1.33{col 50}{space 3}0.184{col 58}{space 4}-.3841237{col 71}{space 3} .0739265
{txt}{space 16} {c |}
{space 10}gender {c |}
{space 9}Female  {c |}{col 18}{res}{space 2} .4437292{col 30}{space 2} .2292101{col 41}{space 1}    1.94{col 50}{space 3}0.053{col 58}{space 4}-.0055143{col 71}{space 3} .8929726
{txt}{space 16} {c |}
{space 8}race_eth {c |}
{space 10}Black  {c |}{col 18}{res}{space 2}-1.468972{col 30}{space 2} .5011909{col 41}{space 1}   -2.93{col 50}{space 3}0.003{col 58}{space 4}-2.451288{col 71}{space 3}-.4866557
{txt}{space 7}Hispanic  {c |}{col 18}{res}{space 2}-.1976297{col 30}{space 2} .4032125{col 41}{space 1}   -0.49{col 50}{space 3}0.624{col 58}{space 4}-.9879116{col 71}{space 3} .5926522
{txt}{space 10}Other  {c |}{col 18}{res}{space 2}-1.449495{col 30}{space 2} .7045541{col 41}{space 1}   -2.06{col 50}{space 3}0.040{col 58}{space 4}-2.830396{col 71}{space 3}-.0685942
{txt}{space 16} {c |}
{space 13}age {c |}{col 18}{res}{space 2} .0134986{col 30}{space 2} .0081528{col 41}{space 1}    1.66{col 50}{space 3}0.098{col 58}{space 4}-.0024807{col 71}{space 3} .0294779
{txt}{space 12}educ {c |}{col 18}{res}{space 2}-.1309035{col 30}{space 2}  .117823{col 41}{space 1}   -1.11{col 50}{space 3}0.267{col 58}{space 4}-.3618324{col 71}{space 3} .1000253
{txt}{space 10}income {c |}{col 18}{res}{space 2} .0325288{col 30}{space 2} .0274013{col 41}{space 1}    1.19{col 50}{space 3}0.235{col 58}{space 4}-.0211768{col 71}{space 3} .0862345
{txt}{space 16} {c |}
{space 9}marital {c |}
{space 8}Married  {c |}{col 18}{res}{space 2}-.1339985{col 30}{space 2} .2574147{col 41}{space 1}   -0.52{col 50}{space 3}0.603{col 58}{space 4}-.6385221{col 71}{space 3}  .370525
{txt}{space 7}evaluate1 {c |}{col 18}{res}{space 2} .3160631{col 30}{space 2} .2899452{col 41}{space 1}    1.09{col 50}{space 3}0.276{col 58}{space 4} -.252219{col 71}{space 3} .8843452
{txt}{space 12}nfc1 {c |}{col 18}{res}{space 2}-.0292995{col 30}{space 2} .1729161{col 41}{space 1}   -0.17{col 50}{space 3}0.865{col 58}{space 4}-.3682089{col 71}{space 3} .3096099
{txt}{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
           /cut1 {c |}{col 18}{res}{space 2}   3.1105{col 30}{space 2} .9082112{col 58}{space 4} 1.330439{col 71}{space 3} 4.890561
{txt}           /cut2 {c |}{col 18}{res}{space 2}  4.22179{col 30}{space 2} .9084603{col 58}{space 4}  2.44124{col 71}{space 3} 6.002339
{txt}           /cut3 {c |}{col 18}{res}{space 2} 6.661865{col 30}{space 2} .9531458{col 58}{space 4} 4.793734{col 71}{space 3} 8.529996
{txt}           /cut4 {c |}{col 18}{res}{space 2} 8.869404{col 30}{space 2} 1.103881{col 58}{space 4} 6.705837{col 71}{space 3} 11.03297
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est6{txt} stored)

{com}. 
. 
. esttab using 2008AMBIV_ALTMEASURES_OUTLIKE.rtf, onecell nobaselevels replace label pr2 aic bic se star(+ 0.10 * 0.05 ** 0.01) ///
>         mtitles("Original Measure" "(/D+A)" "Weigh: by Disc. Interest" ///
>                 "Weigh: by Tie Strength" "Weighted by Gen Dis" "Gen Disagree")  ///
>         title({c -(}\b Table XX.{c )-} "Alternative Measures of Disagreement - 2008-2009 ANES (AMBIVALENCE: OUT-LIKE)") ///
>         rename(disagree_total Disagreement disagree_avg "Disagreement" disagree_total_int Disagreement disagree_total_close Disagreement ///
>                                 disagree_total_weight Disagreement gendiff Disagreement) 
{res}{txt}(output written to {browse  `"2008AMBIV_ALTMEASURES_OUTLIKE.rtf"'})

{com}. 
. /***Knowledge****/
. *W17*
. eststo clear
{txt}
{com}. foreach var in disagree_total disagree_avg disagree_total_int disagree_total_close  ///
>         disagree_total_weight gendiff  {c -(}
{txt}  2{com}.         eststo: logit def_knowl17 i.partisan_9rev##c.`var' numgiven1 network_interest interest_w9 i.gender i.race i.marital ///
>         age educ income nfc1 evaluate1  [pweight= WGTPP17]
{txt}  3{com}. {c )-}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-653.67698}  
Iteration 1:{space 3}log pseudolikelihood = {res:-568.09984}  
Iteration 2:{space 3}log pseudolikelihood = {res:-557.77601}  
Iteration 3:{space 3}log pseudolikelihood = {res:-557.66357}  
Iteration 4:{space 3}log pseudolikelihood = {res: -557.6635}  
Iteration 5:{space 3}log pseudolikelihood = {res: -557.6635}  
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}      1447
{txt}{col 51}Wald chi2({res}16{txt}){col 67}= {res}     83.22
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res} -557.6635{txt}{col 51}Pseudo R2{col 67}= {res}    0.1469

{txt}{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}{col 44}    Robust
{col 1}                   def_knowl17{col 32}{c |}      Coef.{col 44}   Std. Err.{col 56}      z{col 64}   P>|z|{col 72}     [95% Con{col 85}f. Interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}partisan_9rev {c |}
{space 18}In-Partisan  {c |}{col 32}{res}{space 2}-1.059497{col 44}{space 2} .2878111{col 55}{space 1}   -3.68{col 64}{space 3}0.000{col 72}{space 4}-1.623597{col 85}{space 3}-.4953979
{txt}{space 16}disagree_total {c |}{col 32}{res}{space 2}-.0172163{col 44}{space 2} .1094754{col 55}{space 1}   -0.16{col 64}{space 3}0.875{col 72}{space 4}-.2317841{col 85}{space 3} .1973515
{txt}{space 30} {c |}
partisan_9rev#c.disagree_total {c |}
{space 18}In-Partisan  {c |}{col 32}{res}{space 2} .0610493{col 44}{space 2} .1346812{col 55}{space 1}    0.45{col 64}{space 3}0.650{col 72}{space 4}-.2029211{col 85}{space 3} .3250196
{txt}{space 30} {c |}
{space 21}numgiven1 {c |}{col 32}{res}{space 2} .3942594{col 44}{space 2} .2038639{col 55}{space 1}    1.93{col 64}{space 3}0.053{col 72}{space 4}-.0053064{col 85}{space 3} .7938253
{txt}{space 14}network_interest {c |}{col 32}{res}{space 2} -.063321{col 44}{space 2} .1315519{col 55}{space 1}   -0.48{col 64}{space 3}0.630{col 72}{space 4}-.3211579{col 85}{space 3}  .194516
{txt}{space 19}interest_w9 {c |}{col 32}{res}{space 2}-.1209955{col 44}{space 2} .1528665{col 55}{space 1}   -0.79{col 64}{space 3}0.429{col 72}{space 4}-.4206083{col 85}{space 3} .1786174
{txt}{space 30} {c |}
{space 24}gender {c |}
{space 23}Female  {c |}{col 32}{res}{space 2}-.6528869{col 44}{space 2} .2527712{col 55}{space 1}   -2.58{col 64}{space 3}0.010{col 72}{space 4}-1.148309{col 85}{space 3}-.1574645
{txt}{space 30} {c |}
{space 22}race_eth {c |}
{space 24}Black  {c |}{col 32}{res}{space 2}-1.060426{col 44}{space 2} .3114591{col 55}{space 1}   -3.40{col 64}{space 3}0.001{col 72}{space 4}-1.670874{col 85}{space 3}-.4499774
{txt}{space 21}Hispanic  {c |}{col 32}{res}{space 2}-.7228835{col 44}{space 2} .4257603{col 55}{space 1}   -1.70{col 64}{space 3}0.090{col 72}{space 4}-1.557358{col 85}{space 3} .1115914
{txt}{space 24}Other  {c |}{col 32}{res}{space 2}-.4889776{col 44}{space 2} .7088675{col 55}{space 1}   -0.69{col 64}{space 3}0.490{col 72}{space 4}-1.878332{col 85}{space 3} .9003773
{txt}{space 30} {c |}
{space 23}marital {c |}
{space 22}Married  {c |}{col 32}{res}{space 2} .2468029{col 44}{space 2}  .257398{col 55}{space 1}    0.96{col 64}{space 3}0.338{col 72}{space 4} -.257688{col 85}{space 3} .7512938
{txt}{space 27}age {c |}{col 32}{res}{space 2} .0187865{col 44}{space 2} .0081927{col 55}{space 1}    2.29{col 64}{space 3}0.022{col 72}{space 4} .0027291{col 85}{space 3} .0348439
{txt}{space 26}educ {c |}{col 32}{res}{space 2} .0164363{col 44}{space 2} .1421243{col 55}{space 1}    0.12{col 64}{space 3}0.908{col 72}{space 4}-.2621222{col 85}{space 3} .2949948
{txt}{space 24}income {c |}{col 32}{res}{space 2}-.0256288{col 44}{space 2} .0311675{col 55}{space 1}   -0.82{col 64}{space 3}0.411{col 72}{space 4}-.0867159{col 85}{space 3} .0354583
{txt}{space 26}nfc1 {c |}{col 32}{res}{space 2}  .065491{col 44}{space 2} .1730314{col 55}{space 1}    0.38{col 64}{space 3}0.705{col 72}{space 4}-.2736443{col 85}{space 3} .4046262
{txt}{space 21}evaluate1 {c |}{col 32}{res}{space 2} .3051392{col 44}{space 2} .3309887{col 55}{space 1}    0.92{col 64}{space 3}0.357{col 72}{space 4}-.3435868{col 85}{space 3} .9538652
{txt}{space 25}_cons {c |}{col 32}{res}{space 2} 1.218137{col 44}{space 2} .9680723{col 55}{space 1}    1.26{col 64}{space 3}0.208{col 72}{space 4}-.6792496{col 85}{space 3} 3.115524
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-653.67698}  
Iteration 1:{space 3}log pseudolikelihood = {res:-567.64088}  
Iteration 2:{space 3}log pseudolikelihood = {res:-557.25483}  
Iteration 3:{space 3}log pseudolikelihood = {res:-557.14113}  
Iteration 4:{space 3}log pseudolikelihood = {res:-557.14105}  
Iteration 5:{space 3}log pseudolikelihood = {res:-557.14105}  
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}      1447
{txt}{col 51}Wald chi2({res}16{txt}){col 67}= {res}     83.85
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-557.14105{txt}{col 51}Pseudo R2{col 67}= {res}    0.1477

{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                 def_knowl17{col 30}{c |}      Coef.{col 42}   Std. Err.{col 54}      z{col 62}   P>|z|{col 70}     [95% Con{col 83}f. Interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}partisan_9rev {c |}
{space 16}In-Partisan  {c |}{col 30}{res}{space 2}-1.041146{col 42}{space 2} .2842974{col 53}{space 1}   -3.66{col 62}{space 3}0.000{col 70}{space 4}-1.598358{col 83}{space 3}-.4839331
{txt}{space 16}disagree_avg {c |}{col 30}{res}{space 2}-.0378801{col 42}{space 2} .2871035{col 53}{space 1}   -0.13{col 62}{space 3}0.895{col 70}{space 4}-.6005928{col 83}{space 3} .5248325
{txt}{space 28} {c |}
partisan_9rev#c.disagree_avg {c |}
{space 16}In-Partisan  {c |}{col 30}{res}{space 2} .2273184{col 42}{space 2} .3555749{col 53}{space 1}    0.64{col 62}{space 3}0.523{col 70}{space 4}-.4695956{col 83}{space 3} .9242323
{txt}{space 28} {c |}
{space 19}numgiven1 {c |}{col 30}{res}{space 2} .3955872{col 42}{space 2} .2019608{col 53}{space 1}    1.96{col 62}{space 3}0.050{col 70}{space 4}-.0002486{col 83}{space 3} .7914231
{txt}{space 12}network_interest {c |}{col 30}{res}{space 2}-.0593707{col 42}{space 2} .1317745{col 53}{space 1}   -0.45{col 62}{space 3}0.652{col 70}{space 4} -.317644{col 83}{space 3} .1989026
{txt}{space 17}interest_w9 {c |}{col 30}{res}{space 2}-.1256421{col 42}{space 2} .1524948{col 53}{space 1}   -0.82{col 62}{space 3}0.410{col 70}{space 4}-.4245264{col 83}{space 3} .1732423
{txt}{space 28} {c |}
{space 22}gender {c |}
{space 21}Female  {c |}{col 30}{res}{space 2}-.6516427{col 42}{space 2} .2523078{col 53}{space 1}   -2.58{col 62}{space 3}0.010{col 70}{space 4}-1.146157{col 83}{space 3}-.1571285
{txt}{space 28} {c |}
{space 20}race_eth {c |}
{space 22}Black  {c |}{col 30}{res}{space 2}-1.039177{col 42}{space 2} .3097444{col 53}{space 1}   -3.35{col 62}{space 3}0.001{col 70}{space 4}-1.646265{col 83}{space 3}-.4320891
{txt}{space 19}Hispanic  {c |}{col 30}{res}{space 2}-.7273325{col 42}{space 2} .4253987{col 53}{space 1}   -1.71{col 62}{space 3}0.087{col 70}{space 4}-1.561099{col 83}{space 3} .1064335
{txt}{space 22}Other  {c |}{col 30}{res}{space 2}-.5274585{col 42}{space 2} .7087671{col 53}{space 1}   -0.74{col 62}{space 3}0.457{col 70}{space 4}-1.916617{col 83}{space 3} .8616995
{txt}{space 28} {c |}
{space 21}marital {c |}
{space 20}Married  {c |}{col 30}{res}{space 2} .2493922{col 42}{space 2} .2569785{col 53}{space 1}    0.97{col 62}{space 3}0.332{col 70}{space 4}-.2542764{col 83}{space 3} .7530609
{txt}{space 25}age {c |}{col 30}{res}{space 2} .0188437{col 42}{space 2} .0081858{col 53}{space 1}    2.30{col 62}{space 3}0.021{col 70}{space 4} .0027998{col 83}{space 3} .0348877
{txt}{space 24}educ {c |}{col 30}{res}{space 2} .0175073{col 42}{space 2} .1414379{col 53}{space 1}    0.12{col 62}{space 3}0.901{col 70}{space 4}-.2597059{col 83}{space 3} .2947204
{txt}{space 22}income {c |}{col 30}{res}{space 2}-.0260084{col 42}{space 2} .0310394{col 53}{space 1}   -0.84{col 62}{space 3}0.402{col 70}{space 4}-.0868446{col 83}{space 3} .0348277
{txt}{space 24}nfc1 {c |}{col 30}{res}{space 2} .0671295{col 42}{space 2} .1728386{col 53}{space 1}    0.39{col 62}{space 3}0.698{col 70}{space 4} -.271628{col 83}{space 3}  .405887
{txt}{space 19}evaluate1 {c |}{col 30}{res}{space 2} .3099544{col 42}{space 2} .3313758{col 53}{space 1}    0.94{col 62}{space 3}0.350{col 70}{space 4}-.3395302{col 83}{space 3} .9594389
{txt}{space 23}_cons {c |}{col 30}{res}{space 2} 1.210979{col 42}{space 2} .9593657{col 53}{space 1}    1.26{col 62}{space 3}0.207{col 70}{space 4}-.6693434{col 83}{space 3} 3.091301
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-653.67698}  
Iteration 1:{space 3}log pseudolikelihood = {res:-567.80042}  
Iteration 2:{space 3}log pseudolikelihood = {res:-557.46226}  
Iteration 3:{space 3}log pseudolikelihood = {res:-557.34971}  
Iteration 4:{space 3}log pseudolikelihood = {res:-557.34964}  
Iteration 5:{space 3}log pseudolikelihood = {res:-557.34964}  
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}      1447
{txt}{col 51}Wald chi2({res}16{txt}){col 67}= {res}     84.22
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-557.34964{txt}{col 51}Pseudo R2{col 67}= {res}    0.1474

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}                       def_knowl17{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      z{col 68}   P>|z|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}partisan_9rev {c |}
{space 22}In-Partisan  {c |}{col 36}{res}{space 2}-1.031883{col 48}{space 2} .2901406{col 59}{space 1}   -3.56{col 68}{space 3}0.000{col 76}{space 4}-1.600548{col 89}{space 3}-.4632179
{txt}{space 16}disagree_total_int {c |}{col 36}{res}{space 2} -.007478{col 48}{space 2} .0258112{col 59}{space 1}   -0.29{col 68}{space 3}0.772{col 76}{space 4} -.058067{col 89}{space 3} .0431109
{txt}{space 34} {c |}
partisan_9rev#c.disagree_total_int {c |}
{space 22}In-Partisan  {c |}{col 36}{res}{space 2} .0223018{col 48}{space 2} .0321379{col 59}{space 1}    0.69{col 68}{space 3}0.488{col 76}{space 4}-.0406873{col 89}{space 3} .0852909
{txt}{space 34} {c |}
{space 25}numgiven1 {c |}{col 36}{res}{space 2} .3958687{col 48}{space 2} .2033356{col 59}{space 1}    1.95{col 68}{space 3}0.052{col 76}{space 4}-.0026617{col 89}{space 3} .7943992
{txt}{space 18}network_interest {c |}{col 36}{res}{space 2}-.0550043{col 48}{space 2} .1319488{col 59}{space 1}   -0.42{col 68}{space 3}0.677{col 76}{space 4}-.3136192{col 89}{space 3} .2036106
{txt}{space 23}interest_w9 {c |}{col 36}{res}{space 2}-.1185231{col 48}{space 2} .1527144{col 59}{space 1}   -0.78{col 68}{space 3}0.438{col 76}{space 4}-.4178378{col 89}{space 3} .1807916
{txt}{space 34} {c |}
{space 28}gender {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.6488266{col 48}{space 2} .2545149{col 59}{space 1}   -2.55{col 68}{space 3}0.011{col 76}{space 4}-1.147667{col 89}{space 3}-.1499866
{txt}{space 34} {c |}
{space 26}race_eth {c |}
{space 28}Black  {c |}{col 36}{res}{space 2} -1.04248{col 48}{space 2} .3116937{col 59}{space 1}   -3.34{col 68}{space 3}0.001{col 76}{space 4}-1.653389{col 89}{space 3}-.4315718
{txt}{space 25}Hispanic  {c |}{col 36}{res}{space 2} -.730544{col 48}{space 2} .4260676{col 59}{space 1}   -1.71{col 68}{space 3}0.086{col 76}{space 4}-1.565621{col 89}{space 3} .1045331
{txt}{space 28}Other  {c |}{col 36}{res}{space 2}-.4866704{col 48}{space 2} .7163645{col 59}{space 1}   -0.68{col 68}{space 3}0.497{col 76}{space 4}-1.890719{col 89}{space 3} .9173783
{txt}{space 34} {c |}
{space 27}marital {c |}
{space 26}Married  {c |}{col 36}{res}{space 2} .2464336{col 48}{space 2} .2570259{col 59}{space 1}    0.96{col 68}{space 3}0.338{col 76}{space 4} -.257328{col 89}{space 3} .7501952
{txt}{space 31}age {c |}{col 36}{res}{space 2} .0188444{col 48}{space 2} .0081958{col 59}{space 1}    2.30{col 68}{space 3}0.021{col 76}{space 4} .0027809{col 89}{space 3} .0349079
{txt}{space 30}educ {c |}{col 36}{res}{space 2} .0166735{col 48}{space 2} .1419797{col 59}{space 1}    0.12{col 68}{space 3}0.907{col 76}{space 4}-.2616017{col 89}{space 3} .2949487
{txt}{space 28}income {c |}{col 36}{res}{space 2}-.0251699{col 48}{space 2} .0311927{col 59}{space 1}   -0.81{col 68}{space 3}0.420{col 76}{space 4}-.0863066{col 89}{space 3} .0359667
{txt}{space 30}nfc1 {c |}{col 36}{res}{space 2} .0672131{col 48}{space 2} .1737647{col 59}{space 1}    0.39{col 68}{space 3}0.699{col 76}{space 4}-.2733595{col 89}{space 3} .4077857
{txt}{space 25}evaluate1 {c |}{col 36}{res}{space 2} .3078506{col 48}{space 2} .3297457{col 59}{space 1}    0.93{col 68}{space 3}0.351{col 76}{space 4} -.338439{col 89}{space 3} .9541402
{txt}{space 29}_cons {c |}{col 36}{res}{space 2} 1.145925{col 48}{space 2} .9738058{col 59}{space 1}    1.18{col 68}{space 3}0.239{col 76}{space 4}-.7626996{col 89}{space 3} 3.054549
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-653.67698}  
Iteration 1:{space 3}log pseudolikelihood = {res:-568.36155}  
Iteration 2:{space 3}log pseudolikelihood = {res:-558.08583}  
Iteration 3:{space 3}log pseudolikelihood = {res: -557.9742}  
Iteration 4:{space 3}log pseudolikelihood = {res:-557.97412}  
Iteration 5:{space 3}log pseudolikelihood = {res:-557.97412}  
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}      1447
{txt}{col 51}Wald chi2({res}16{txt}){col 67}= {res}     82.17
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-557.97412{txt}{col 51}Pseudo R2{col 67}= {res}    0.1464

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}           def_knowl17{col 24}{c |}      Coef.{col 36}   Std. Err.{col 48}      z{col 56}   P>|z|{col 64}     [95% Con{col 77}f. Interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}partisan_9rev {c |}
{space 10}In-Partisan  {c |}{col 24}{res}{space 2}-1.094251{col 36}{space 2} .2937161{col 47}{space 1}   -3.73{col 56}{space 3}0.000{col 64}{space 4}-1.669924{col 77}{space 3}-.5185782
{txt}{space 2}disagree_total_close {c |}{col 24}{res}{space 2}  .000379{col 36}{space 2} .0278728{col 47}{space 1}    0.01{col 56}{space 3}0.989{col 64}{space 4}-.0542507{col 77}{space 3} .0550087
{txt}{space 22} {c |}
{space 9}partisan_9rev#{c |}
c.disagree_total_close {c |}
{space 10}In-Partisan  {c |}{col 24}{res}{space 2} .0047298{col 36}{space 2} .0335573{col 47}{space 1}    0.14{col 56}{space 3}0.888{col 64}{space 4}-.0610414{col 77}{space 3} .0705009
{txt}{space 22} {c |}
{space 13}numgiven1 {c |}{col 24}{res}{space 2} .3894576{col 36}{space 2} .2039731{col 47}{space 1}    1.91{col 56}{space 3}0.056{col 64}{space 4}-.0103224{col 77}{space 3} .7892376
{txt}{space 6}network_interest {c |}{col 24}{res}{space 2}-.0654913{col 36}{space 2} .1313879{col 47}{space 1}   -0.50{col 56}{space 3}0.618{col 64}{space 4}-.3230068{col 77}{space 3} .1920242
{txt}{space 11}interest_w9 {c |}{col 24}{res}{space 2}-.1179743{col 36}{space 2} .1527898{col 47}{space 1}   -0.77{col 56}{space 3}0.440{col 64}{space 4}-.4174368{col 77}{space 3} .1814882
{txt}{space 22} {c |}
{space 16}gender {c |}
{space 15}Female  {c |}{col 24}{res}{space 2}-.6535486{col 36}{space 2} .2537086{col 47}{space 1}   -2.58{col 56}{space 3}0.010{col 64}{space 4}-1.150808{col 77}{space 3}-.1562888
{txt}{space 22} {c |}
{space 14}race_eth {c |}
{space 16}Black  {c |}{col 24}{res}{space 2}-1.086828{col 36}{space 2} .3113268{col 47}{space 1}   -3.49{col 56}{space 3}0.000{col 64}{space 4}-1.697017{col 77}{space 3}-.4766385
{txt}{space 13}Hispanic  {c |}{col 24}{res}{space 2}-.7251171{col 36}{space 2} .4242144{col 47}{space 1}   -1.71{col 56}{space 3}0.087{col 64}{space 4}-1.556562{col 77}{space 3} .1063277
{txt}{space 16}Other  {c |}{col 24}{res}{space 2}-.4470161{col 36}{space 2} .7204683{col 47}{space 1}   -0.62{col 56}{space 3}0.535{col 64}{space 4}-1.859108{col 77}{space 3} .9650758
{txt}{space 22} {c |}
{space 15}marital {c |}
{space 14}Married  {c |}{col 24}{res}{space 2} .2477864{col 36}{space 2} .2572663{col 47}{space 1}    0.96{col 56}{space 3}0.335{col 64}{space 4}-.2564462{col 77}{space 3} .7520191
{txt}{space 19}age {c |}{col 24}{res}{space 2} .0186193{col 36}{space 2} .0081734{col 47}{space 1}    2.28{col 56}{space 3}0.023{col 64}{space 4} .0025997{col 77}{space 3}  .034639
{txt}{space 18}educ {c |}{col 24}{res}{space 2} .0123848{col 36}{space 2} .1421331{col 47}{space 1}    0.09{col 56}{space 3}0.931{col 64}{space 4}-.2661909{col 77}{space 3} .2909605
{txt}{space 16}income {c |}{col 24}{res}{space 2}-.0253067{col 36}{space 2} .0312592{col 47}{space 1}   -0.81{col 56}{space 3}0.418{col 64}{space 4}-.0865735{col 77}{space 3} .0359601
{txt}{space 18}nfc1 {c |}{col 24}{res}{space 2} .0697773{col 36}{space 2} .1726157{col 47}{space 1}    0.40{col 56}{space 3}0.686{col 64}{space 4}-.2685432{col 77}{space 3} .4080978
{txt}{space 13}evaluate1 {c |}{col 24}{res}{space 2} .2910748{col 36}{space 2}  .330587{col 47}{space 1}    0.88{col 56}{space 3}0.379{col 64}{space 4}-.3568638{col 77}{space 3} .9390133
{txt}{space 17}_cons {c |}{col 24}{res}{space 2} 1.269324{col 36}{space 2} .9681651{col 47}{space 1}    1.31{col 56}{space 3}0.190{col 64}{space 4}-.6282451{col 77}{space 3} 3.166892
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-653.67698}  
Iteration 1:{space 3}log pseudolikelihood = {res:-568.29933}  
Iteration 2:{space 3}log pseudolikelihood = {res:-557.98574}  
Iteration 3:{space 3}log pseudolikelihood = {res: -557.8734}  
Iteration 4:{space 3}log pseudolikelihood = {res:-557.87332}  
Iteration 5:{space 3}log pseudolikelihood = {res:-557.87332}  
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}      1447
{txt}{col 51}Wald chi2({res}16{txt}){col 67}= {res}     82.35
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-557.87332{txt}{col 51}Pseudo R2{col 67}= {res}    0.1466

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}            def_knowl17{col 25}{c |}      Coef.{col 37}   Std. Err.{col 49}      z{col 57}   P>|z|{col 65}     [95% Con{col 78}f. Interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}partisan_9rev {c |}
{space 11}In-Partisan  {c |}{col 25}{res}{space 2} -1.05197{col 37}{space 2} .3020795{col 48}{space 1}   -3.48{col 57}{space 3}0.000{col 65}{space 4}-1.644034{col 78}{space 3}-.4599045
{txt}{space 2}disagree_total_weight {c |}{col 25}{res}{space 2}-.0049049{col 37}{space 2} .0251043{col 48}{space 1}   -0.20{col 57}{space 3}0.845{col 65}{space 4}-.0541084{col 78}{space 3} .0442986
{txt}{space 23} {c |}
{space 10}partisan_9rev#{c |}
c.disagree_total_weight {c |}
{space 11}In-Partisan  {c |}{col 25}{res}{space 2} .0128685{col 37}{space 2} .0323949{col 48}{space 1}    0.40{col 57}{space 3}0.691{col 65}{space 4}-.0506244{col 78}{space 3} .0763614
{txt}{space 23} {c |}
{space 14}numgiven1 {c |}{col 25}{res}{space 2} .3914479{col 37}{space 2} .2051888{col 48}{space 1}    1.91{col 57}{space 3}0.056{col 65}{space 4}-.0107148{col 78}{space 3} .7936106
{txt}{space 7}network_interest {c |}{col 25}{res}{space 2}-.0656383{col 37}{space 2} .1314679{col 48}{space 1}   -0.50{col 57}{space 3}0.618{col 65}{space 4}-.3233107{col 78}{space 3} .1920341
{txt}{space 12}interest_w9 {c |}{col 25}{res}{space 2}-.1186489{col 37}{space 2} .1526439{col 48}{space 1}   -0.78{col 57}{space 3}0.437{col 65}{space 4}-.4178255{col 78}{space 3} .1805277
{txt}{space 23} {c |}
{space 17}gender {c |}
{space 16}Female  {c |}{col 25}{res}{space 2}-.6553749{col 37}{space 2} .2535971{col 48}{space 1}   -2.58{col 57}{space 3}0.010{col 65}{space 4}-1.152416{col 78}{space 3}-.1583337
{txt}{space 23} {c |}
{space 15}race_eth {c |}
{space 17}Black  {c |}{col 25}{res}{space 2}-1.075884{col 37}{space 2} .3107239{col 48}{space 1}   -3.46{col 57}{space 3}0.001{col 65}{space 4}-1.684892{col 78}{space 3}-.4668761
{txt}{space 14}Hispanic  {c |}{col 25}{res}{space 2}-.7207632{col 37}{space 2} .4254107{col 48}{space 1}   -1.69{col 57}{space 3}0.090{col 65}{space 4}-1.554553{col 78}{space 3} .1130264
{txt}{space 17}Other  {c |}{col 25}{res}{space 2} -.453136{col 37}{space 2} .7229733{col 48}{space 1}   -0.63{col 57}{space 3}0.531{col 65}{space 4}-1.870138{col 78}{space 3} .9638656
{txt}{space 23} {c |}
{space 16}marital {c |}
{space 15}Married  {c |}{col 25}{res}{space 2}  .245936{col 37}{space 2} .2572293{col 48}{space 1}    0.96{col 57}{space 3}0.339{col 65}{space 4}-.2582241{col 78}{space 3} .7500961
{txt}{space 20}age {c |}{col 25}{res}{space 2} .0188191{col 37}{space 2}  .008203{col 48}{space 1}    2.29{col 57}{space 3}0.022{col 65}{space 4} .0027415{col 78}{space 3} .0348967
{txt}{space 19}educ {c |}{col 25}{res}{space 2} .0114609{col 37}{space 2}   .14115{col 48}{space 1}    0.08{col 57}{space 3}0.935{col 65}{space 4} -.265188{col 78}{space 3} .2881099
{txt}{space 17}income {c |}{col 25}{res}{space 2} -.025112{col 37}{space 2} .0313623{col 48}{space 1}   -0.80{col 57}{space 3}0.423{col 65}{space 4} -.086581{col 78}{space 3} .0363571
{txt}{space 19}nfc1 {c |}{col 25}{res}{space 2} .0701322{col 37}{space 2} .1733227{col 48}{space 1}    0.40{col 57}{space 3}0.686{col 65}{space 4}-.2695741{col 78}{space 3} .4098384
{txt}{space 14}evaluate1 {c |}{col 25}{res}{space 2} .2968495{col 37}{space 2} .3300838{col 48}{space 1}    0.90{col 57}{space 3}0.368{col 65}{space 4} -.350103{col 78}{space 3} .9438019
{txt}{space 18}_cons {c |}{col 25}{res}{space 2} 1.228451{col 37}{space 2} .9723008{col 48}{space 1}    1.26{col 57}{space 3}0.206{col 65}{space 4}-.6772232{col 78}{space 3} 3.134126
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est5{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-653.67698}  
Iteration 1:{space 3}log pseudolikelihood = {res:-568.39361}  
Iteration 2:{space 3}log pseudolikelihood = {res:-558.11225}  
Iteration 3:{space 3}log pseudolikelihood = {res:-558.00011}  
Iteration 4:{space 3}log pseudolikelihood = {res:-558.00003}  
Iteration 5:{space 3}log pseudolikelihood = {res:-558.00003}  
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}      1447
{txt}{col 51}Wald chi2({res}16{txt}){col 67}= {res}     81.01
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-558.00003{txt}{col 51}Pseudo R2{col 67}= {res}    0.1464

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}            def_knowl17{col 25}{c |}      Coef.{col 37}   Std. Err.{col 49}      z{col 57}   P>|z|{col 65}     [95% Con{col 78}f. Interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}partisan_9rev {c |}
{space 11}In-Partisan  {c |}{col 25}{res}{space 2} -.993749{col 37}{space 2}  .624512{col 48}{space 1}   -1.59{col 57}{space 3}0.112{col 65}{space 4} -2.21777{col 78}{space 3}  .230272
{txt}{space 16}gendiff {c |}{col 25}{res}{space 2} .0156704{col 37}{space 2} .1862383{col 48}{space 1}    0.08{col 57}{space 3}0.933{col 65}{space 4}  -.34935{col 78}{space 3} .3806908
{txt}{space 23} {c |}
partisan_9rev#c.gendiff {c |}
{space 11}In-Partisan  {c |}{col 25}{res}{space 2}-.0540245{col 37}{space 2} .2456177{col 48}{space 1}   -0.22{col 57}{space 3}0.826{col 65}{space 4}-.5354262{col 78}{space 3} .4273773
{txt}{space 23} {c |}
{space 14}numgiven1 {c |}{col 25}{res}{space 2} .3807564{col 37}{space 2} .2031286{col 48}{space 1}    1.87{col 57}{space 3}0.061{col 65}{space 4}-.0173683{col 78}{space 3} .7788812
{txt}{space 7}network_interest {c |}{col 25}{res}{space 2}-.0700018{col 37}{space 2} .1310613{col 48}{space 1}   -0.53{col 57}{space 3}0.593{col 65}{space 4}-.3268773{col 78}{space 3} .1868736
{txt}{space 12}interest_w9 {c |}{col 25}{res}{space 2}-.1147964{col 37}{space 2} .1520866{col 48}{space 1}   -0.75{col 57}{space 3}0.450{col 65}{space 4}-.4128807{col 78}{space 3} .1832879
{txt}{space 23} {c |}
{space 17}gender {c |}
{space 16}Female  {c |}{col 25}{res}{space 2}-.6594526{col 37}{space 2} .2538104{col 48}{space 1}   -2.60{col 57}{space 3}0.009{col 65}{space 4}-1.156912{col 78}{space 3}-.1619933
{txt}{space 23} {c |}
{space 15}race_eth {c |}
{space 17}Black  {c |}{col 25}{res}{space 2} -1.11608{col 37}{space 2} .2964501{col 48}{space 1}   -3.76{col 57}{space 3}0.000{col 65}{space 4}-1.697112{col 78}{space 3}-.5350485
{txt}{space 14}Hispanic  {c |}{col 25}{res}{space 2}-.7172188{col 37}{space 2} .4259598{col 48}{space 1}   -1.68{col 57}{space 3}0.092{col 65}{space 4}-1.552085{col 78}{space 3}  .117647
{txt}{space 17}Other  {c |}{col 25}{res}{space 2}-.4271659{col 37}{space 2} .7310199{col 48}{space 1}   -0.58{col 57}{space 3}0.559{col 65}{space 4}-1.859939{col 78}{space 3} 1.005607
{txt}{space 23} {c |}
{space 16}marital {c |}
{space 15}Married  {c |}{col 25}{res}{space 2} .2491039{col 37}{space 2} .2553866{col 48}{space 1}    0.98{col 57}{space 3}0.329{col 65}{space 4}-.2514446{col 78}{space 3} .7496524
{txt}{space 20}age {c |}{col 25}{res}{space 2} .0183522{col 37}{space 2} .0080988{col 48}{space 1}    2.27{col 57}{space 3}0.023{col 65}{space 4} .0024788{col 78}{space 3} .0342256
{txt}{space 19}educ {c |}{col 25}{res}{space 2} .0147705{col 37}{space 2} .1412173{col 48}{space 1}    0.10{col 57}{space 3}0.917{col 65}{space 4}-.2620102{col 78}{space 3} .2915513
{txt}{space 17}income {c |}{col 25}{res}{space 2}-.0259323{col 37}{space 2} .0312965{col 48}{space 1}   -0.83{col 57}{space 3}0.407{col 65}{space 4}-.0872724{col 78}{space 3} .0354077
{txt}{space 19}nfc1 {c |}{col 25}{res}{space 2} .0697131{col 37}{space 2} .1717461{col 48}{space 1}    0.41{col 57}{space 3}0.685{col 65}{space 4}-.2669031{col 78}{space 3} .4063292
{txt}{space 14}evaluate1 {c |}{col 25}{res}{space 2} .2778666{col 37}{space 2} .3294305{col 48}{space 1}    0.84{col 57}{space 3}0.399{col 65}{space 4}-.3678052{col 78}{space 3} .9235385
{txt}{space 18}_cons {c |}{col 25}{res}{space 2} 1.293679{col 37}{space 2} 1.058155{col 48}{space 1}    1.22{col 57}{space 3}0.221{col 65}{space 4} -.780266{col 78}{space 3} 3.367625
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est6{txt} stored)

{com}. 
. esttab using 2008KNOWL_ALTMEASURES_MAY.rtf, onecell nobaselevels replace label pr2 aic bic se star(+ 0.10 * 0.05 ** 0.01) ///
>         mtitles("Original Measure" "(/D+A)" "Weigh: by Disc. Interest" ///
>                 "Weigh: by Tie Strength" "Weighted by Gen Dis" "Gen Disagree")  ///
>         title({c -(}\b Table XX.{c )-} "Alternative Measures of Disagreement - 2008-2009 ANES (KNOWLEDGE: MAY)") ///
>         rename(disagree_total Disagreement disagree_avg "Disagreement" disagree_total_int Disagreement disagree_total_close Disagreement ///
>                                 disagree_total_weight Disagreement gendiff Disagreement ///
>                                 1.partisan_9rev#c.disagree_total Partisan*Disagree ///
>                                 1.partisan_9rev#c.disagree_avg Partisan*Disagree ///
>                                 1.partisan_9rev#c.disagree_total_int Partisan*Disagree ///
>                                 1.partisan_9rev#c.disagree_total_close Partisan*Disagree ///
>                                 1.partisan_9rev#c.disagree_total_weight Partisan*Disagree ///
>                                 1.partisan_9rev#c.gendiff Partisan*Disagree)
{res}{txt}(output written to {browse  `"2008KNOWL_ALTMEASURES_MAY.rtf"'})

{com}. 
. *W19
. eststo clear
{txt}
{com}. foreach var in disagree_total disagree_avg disagree_total_int disagree_total_close  ///
>         disagree_total_weight gendiff  {c -(}
{txt}  2{com}.         eststo: logit def_knowl19 i.partisan_9rev##c.`var' ///
>         numgiven1 network_interest interest_w9 i.gender i.race i.marital age educ income nfc1 evaluate1 [pweight= WGTPP19]
{txt}  3{com}.                 {c )-}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-626.92863}  
Iteration 1:{space 3}log pseudolikelihood = {res:-504.21338}  
Iteration 2:{space 3}log pseudolikelihood = {res:-487.40265}  
Iteration 3:{space 3}log pseudolikelihood = {res:-486.96406}  
Iteration 4:{space 3}log pseudolikelihood = {res:-486.96287}  
Iteration 5:{space 3}log pseudolikelihood = {res:-486.96287}  
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}      1353
{txt}{col 51}Wald chi2({res}16{txt}){col 67}= {res}    121.93
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-486.96287{txt}{col 51}Pseudo R2{col 67}= {res}    0.2233

{txt}{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}{col 44}    Robust
{col 1}                   def_knowl19{col 32}{c |}      Coef.{col 44}   Std. Err.{col 56}      z{col 64}   P>|z|{col 72}     [95% Con{col 85}f. Interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}partisan_9rev {c |}
{space 18}In-Partisan  {c |}{col 32}{res}{space 2}   -1.263{col 44}{space 2} .3226996{col 55}{space 1}   -3.91{col 64}{space 3}0.000{col 72}{space 4}-1.895479{col 85}{space 3}-.6305199
{txt}{space 16}disagree_total {c |}{col 32}{res}{space 2}-.0918354{col 44}{space 2} .1414617{col 55}{space 1}   -0.65{col 64}{space 3}0.516{col 72}{space 4}-.3690953{col 85}{space 3} .1854245
{txt}{space 30} {c |}
partisan_9rev#c.disagree_total {c |}
{space 18}In-Partisan  {c |}{col 32}{res}{space 2} .2960713{col 44}{space 2} .1612069{col 55}{space 1}    1.84{col 64}{space 3}0.066{col 72}{space 4}-.0198883{col 85}{space 3}  .612031
{txt}{space 30} {c |}
{space 21}numgiven1 {c |}{col 32}{res}{space 2}-.7076202{col 44}{space 2}  .284167{col 55}{space 1}   -2.49{col 64}{space 3}0.013{col 72}{space 4}-1.264577{col 85}{space 3}-.1506632
{txt}{space 14}network_interest {c |}{col 32}{res}{space 2}-.2669301{col 44}{space 2} .1453364{col 55}{space 1}   -1.84{col 64}{space 3}0.066{col 72}{space 4}-.5517843{col 85}{space 3} .0179241
{txt}{space 19}interest_w9 {c |}{col 32}{res}{space 2} .2160632{col 44}{space 2} .1427883{col 55}{space 1}    1.51{col 64}{space 3}0.130{col 72}{space 4}-.0637967{col 85}{space 3} .4959231
{txt}{space 30} {c |}
{space 24}gender {c |}
{space 23}Female  {c |}{col 32}{res}{space 2}-.3537547{col 44}{space 2} .2463003{col 55}{space 1}   -1.44{col 64}{space 3}0.151{col 72}{space 4}-.8364945{col 85}{space 3} .1289852
{txt}{space 30} {c |}
{space 22}race_eth {c |}
{space 24}Black  {c |}{col 32}{res}{space 2}-1.366899{col 44}{space 2} .3287573{col 55}{space 1}   -4.16{col 64}{space 3}0.000{col 72}{space 4}-2.011252{col 85}{space 3}-.7225468
{txt}{space 21}Hispanic  {c |}{col 32}{res}{space 2}-.6113394{col 44}{space 2} .4475739{col 55}{space 1}   -1.37{col 64}{space 3}0.172{col 72}{space 4}-1.488568{col 85}{space 3} .2658893
{txt}{space 24}Other  {c |}{col 32}{res}{space 2}-.2263005{col 44}{space 2} .5981559{col 55}{space 1}   -0.38{col 64}{space 3}0.705{col 72}{space 4}-1.398665{col 85}{space 3} .9460636
{txt}{space 30} {c |}
{space 23}marital {c |}
{space 22}Married  {c |}{col 32}{res}{space 2}-.0974405{col 44}{space 2} .2537394{col 55}{space 1}   -0.38{col 64}{space 3}0.701{col 72}{space 4}-.5947606{col 85}{space 3} .3998796
{txt}{space 27}age {c |}{col 32}{res}{space 2} .0217794{col 44}{space 2}  .007316{col 55}{space 1}    2.98{col 64}{space 3}0.003{col 72}{space 4} .0074403{col 85}{space 3} .0361185
{txt}{space 26}educ {c |}{col 32}{res}{space 2} .0511377{col 44}{space 2} .1303007{col 55}{space 1}    0.39{col 64}{space 3}0.695{col 72}{space 4}-.2042469{col 85}{space 3} .3065223
{txt}{space 24}income {c |}{col 32}{res}{space 2}-.0157453{col 44}{space 2} .0346869{col 55}{space 1}   -0.45{col 64}{space 3}0.650{col 72}{space 4}-.0837304{col 85}{space 3} .0522398
{txt}{space 26}nfc1 {c |}{col 32}{res}{space 2} .2005319{col 44}{space 2} .1937411{col 55}{space 1}    1.04{col 64}{space 3}0.301{col 72}{space 4}-.1791936{col 85}{space 3} .5802574
{txt}{space 21}evaluate1 {c |}{col 32}{res}{space 2} -.070734{col 44}{space 2} .3135615{col 55}{space 1}   -0.23{col 64}{space 3}0.822{col 72}{space 4}-.6853033{col 85}{space 3} .5438352
{txt}{space 25}_cons {c |}{col 32}{res}{space 2} 4.115462{col 44}{space 2} 1.197519{col 55}{space 1}    3.44{col 64}{space 3}0.001{col 72}{space 4} 1.768369{col 85}{space 3} 6.462555
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-626.92863}  
Iteration 1:{space 3}log pseudolikelihood = {res:-503.66727}  
Iteration 2:{space 3}log pseudolikelihood = {res:-486.69696}  
Iteration 3:{space 3}log pseudolikelihood = {res:-486.23645}  
Iteration 4:{space 3}log pseudolikelihood = {res: -486.2351}  
Iteration 5:{space 3}log pseudolikelihood = {res: -486.2351}  
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}      1353
{txt}{col 51}Wald chi2({res}16{txt}){col 67}= {res}    121.85
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res} -486.2351{txt}{col 51}Pseudo R2{col 67}= {res}    0.2244

{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                 def_knowl19{col 30}{c |}      Coef.{col 42}   Std. Err.{col 54}      z{col 62}   P>|z|{col 70}     [95% Con{col 83}f. Interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}partisan_9rev {c |}
{space 16}In-Partisan  {c |}{col 30}{res}{space 2}-1.255444{col 42}{space 2}  .321899{col 53}{space 1}   -3.90{col 62}{space 3}0.000{col 70}{space 4}-1.886354{col 83}{space 3}-.6245331
{txt}{space 16}disagree_avg {c |}{col 30}{res}{space 2}-.2618599{col 42}{space 2} .3979101{col 53}{space 1}   -0.66{col 62}{space 3}0.510{col 70}{space 4}-1.041749{col 83}{space 3} .5180297
{txt}{space 28} {c |}
partisan_9rev#c.disagree_avg {c |}
{space 16}In-Partisan  {c |}{col 30}{res}{space 2}  .877806{col 42}{space 2} .4550457{col 53}{space 1}    1.93{col 62}{space 3}0.054{col 70}{space 4}-.0140671{col 83}{space 3} 1.769679
{txt}{space 28} {c |}
{space 19}numgiven1 {c |}{col 30}{res}{space 2}-.7504375{col 42}{space 2} .2835171{col 53}{space 1}   -2.65{col 62}{space 3}0.008{col 70}{space 4}-1.306121{col 83}{space 3}-.1947542
{txt}{space 12}network_interest {c |}{col 30}{res}{space 2}-.2619178{col 42}{space 2} .1462369{col 53}{space 1}   -1.79{col 62}{space 3}0.073{col 70}{space 4}-.5485368{col 83}{space 3} .0247012
{txt}{space 17}interest_w9 {c |}{col 30}{res}{space 2} .2113158{col 42}{space 2} .1435907{col 53}{space 1}    1.47{col 62}{space 3}0.141{col 70}{space 4}-.0701168{col 83}{space 3} .4927484
{txt}{space 28} {c |}
{space 22}gender {c |}
{space 21}Female  {c |}{col 30}{res}{space 2} -.361283{col 42}{space 2} .2473113{col 53}{space 1}   -1.46{col 62}{space 3}0.144{col 70}{space 4}-.8460042{col 83}{space 3} .1234382
{txt}{space 28} {c |}
{space 20}race_eth {c |}
{space 22}Black  {c |}{col 30}{res}{space 2}-1.364324{col 42}{space 2} .3265647{col 53}{space 1}   -4.18{col 62}{space 3}0.000{col 70}{space 4}-2.004379{col 83}{space 3}-.7242685
{txt}{space 19}Hispanic  {c |}{col 30}{res}{space 2}-.6086154{col 42}{space 2} .4472167{col 53}{space 1}   -1.36{col 62}{space 3}0.174{col 70}{space 4}-1.485144{col 83}{space 3} .2679132
{txt}{space 22}Other  {c |}{col 30}{res}{space 2}-.2370581{col 42}{space 2} .5978618{col 53}{space 1}   -0.40{col 62}{space 3}0.692{col 70}{space 4}-1.408846{col 83}{space 3} .9347296
{txt}{space 28} {c |}
{space 21}marital {c |}
{space 20}Married  {c |}{col 30}{res}{space 2}-.0823669{col 42}{space 2} .2538194{col 53}{space 1}   -0.32{col 62}{space 3}0.746{col 70}{space 4}-.5798438{col 83}{space 3}   .41511
{txt}{space 25}age {c |}{col 30}{res}{space 2} .0219258{col 42}{space 2} .0073032{col 53}{space 1}    3.00{col 62}{space 3}0.003{col 70}{space 4} .0076118{col 83}{space 3} .0362398
{txt}{space 24}educ {c |}{col 30}{res}{space 2} .0444118{col 42}{space 2} .1300378{col 53}{space 1}    0.34{col 62}{space 3}0.733{col 70}{space 4}-.2104576{col 83}{space 3} .2992813
{txt}{space 22}income {c |}{col 30}{res}{space 2}-.0166142{col 42}{space 2} .0346829{col 53}{space 1}   -0.48{col 62}{space 3}0.632{col 70}{space 4}-.0845913{col 83}{space 3}  .051363
{txt}{space 24}nfc1 {c |}{col 30}{res}{space 2} .2138787{col 42}{space 2} .1932692{col 53}{space 1}    1.11{col 62}{space 3}0.268{col 70}{space 4} -.164922{col 83}{space 3} .5926793
{txt}{space 19}evaluate1 {c |}{col 30}{res}{space 2}-.0834089{col 42}{space 2} .3137797{col 53}{space 1}   -0.27{col 62}{space 3}0.790{col 70}{space 4}-.6984058{col 83}{space 3} .5315881
{txt}{space 23}_cons {c |}{col 30}{res}{space 2} 4.258228{col 42}{space 2} 1.191433{col 53}{space 1}    3.57{col 62}{space 3}0.000{col 70}{space 4} 1.923061{col 83}{space 3} 6.593395
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-626.92863}  
Iteration 1:{space 3}log pseudolikelihood = {res:-501.54192}  
Iteration 2:{space 3}log pseudolikelihood = {res:-484.94989}  
Iteration 3:{space 3}log pseudolikelihood = {res:-484.53685}  
Iteration 4:{space 3}log pseudolikelihood = {res:-484.53583}  
Iteration 5:{space 3}log pseudolikelihood = {res:-484.53583}  
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}      1353
{txt}{col 51}Wald chi2({res}16{txt}){col 67}= {res}    128.21
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-484.53583{txt}{col 51}Pseudo R2{col 67}= {res}    0.2271

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}                       def_knowl19{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      z{col 68}   P>|z|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}partisan_9rev {c |}
{space 22}In-Partisan  {c |}{col 36}{res}{space 2}-1.204951{col 48}{space 2} .3388822{col 59}{space 1}   -3.56{col 68}{space 3}0.000{col 76}{space 4}-1.869147{col 89}{space 3}-.5407536
{txt}{space 16}disagree_total_int {c |}{col 36}{res}{space 2}-.0157252{col 48}{space 2} .0328975{col 59}{space 1}   -0.48{col 68}{space 3}0.633{col 76}{space 4}-.0802031{col 89}{space 3} .0487526
{txt}{space 34} {c |}
partisan_9rev#c.disagree_total_int {c |}
{space 22}In-Partisan  {c |}{col 36}{res}{space 2} .0793758{col 48}{space 2} .0390892{col 59}{space 1}    2.03{col 68}{space 3}0.042{col 76}{space 4} .0027623{col 89}{space 3} .1559892
{txt}{space 34} {c |}
{space 25}numgiven1 {c |}{col 36}{res}{space 2}-.6906721{col 48}{space 2} .2851586{col 59}{space 1}   -2.42{col 68}{space 3}0.015{col 76}{space 4}-1.249573{col 89}{space 3}-.1317715
{txt}{space 18}network_interest {c |}{col 36}{res}{space 2}-.1913467{col 48}{space 2} .1447308{col 59}{space 1}   -1.32{col 68}{space 3}0.186{col 76}{space 4}-.4750138{col 89}{space 3} .0923203
{txt}{space 23}interest_w9 {c |}{col 36}{res}{space 2} .2211819{col 48}{space 2} .1428501{col 59}{space 1}    1.55{col 68}{space 3}0.122{col 76}{space 4}-.0587992{col 89}{space 3} .5011629
{txt}{space 34} {c |}
{space 28}gender {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.3452991{col 48}{space 2} .2475982{col 59}{space 1}   -1.39{col 68}{space 3}0.163{col 76}{space 4}-.8305826{col 89}{space 3} .1399844
{txt}{space 34} {c |}
{space 26}race_eth {c |}
{space 28}Black  {c |}{col 36}{res}{space 2}-1.333898{col 48}{space 2} .3333041{col 59}{space 1}   -4.00{col 68}{space 3}0.000{col 76}{space 4}-1.987162{col 89}{space 3}-.6806337
{txt}{space 25}Hispanic  {c |}{col 36}{res}{space 2}-.6648463{col 48}{space 2} .4520937{col 59}{space 1}   -1.47{col 68}{space 3}0.141{col 76}{space 4}-1.550934{col 89}{space 3} .2212411
{txt}{space 28}Other  {c |}{col 36}{res}{space 2}-.3020127{col 48}{space 2}  .595695{col 59}{space 1}   -0.51{col 68}{space 3}0.612{col 76}{space 4}-1.469553{col 89}{space 3} .8655279
{txt}{space 34} {c |}
{space 27}marital {c |}
{space 26}Married  {c |}{col 36}{res}{space 2}-.0946876{col 48}{space 2} .2561905{col 59}{space 1}   -0.37{col 68}{space 3}0.712{col 76}{space 4}-.5968116{col 89}{space 3} .4074365
{txt}{space 31}age {c |}{col 36}{res}{space 2}  .021632{col 48}{space 2}  .007261{col 59}{space 1}    2.98{col 68}{space 3}0.003{col 76}{space 4} .0074007{col 89}{space 3} .0358633
{txt}{space 30}educ {c |}{col 36}{res}{space 2} .0558472{col 48}{space 2} .1285203{col 59}{space 1}    0.43{col 68}{space 3}0.664{col 76}{space 4} -.196048{col 89}{space 3} .3077424
{txt}{space 28}income {c |}{col 36}{res}{space 2} -.015364{col 48}{space 2} .0344884{col 59}{space 1}   -0.45{col 68}{space 3}0.656{col 76}{space 4}  -.08296{col 89}{space 3} .0522321
{txt}{space 30}nfc1 {c |}{col 36}{res}{space 2}  .201513{col 48}{space 2} .1945501{col 59}{space 1}    1.04{col 68}{space 3}0.300{col 76}{space 4}-.1797981{col 89}{space 3} .5828241
{txt}{space 25}evaluate1 {c |}{col 36}{res}{space 2} -.051666{col 48}{space 2} .3144298{col 59}{space 1}   -0.16{col 68}{space 3}0.869{col 76}{space 4}-.6679371{col 89}{space 3} .5646051
{txt}{space 29}_cons {c |}{col 36}{res}{space 2} 3.737789{col 48}{space 2} 1.215791{col 59}{space 1}    3.07{col 68}{space 3}0.002{col 76}{space 4} 1.354882{col 89}{space 3} 6.120695
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-626.92863}  
Iteration 1:{space 3}log pseudolikelihood = {res:-505.49071}  
Iteration 2:{space 3}log pseudolikelihood = {res:-488.73744}  
Iteration 3:{space 3}log pseudolikelihood = {res:-488.29291}  
Iteration 4:{space 3}log pseudolikelihood = {res:-488.29164}  
Iteration 5:{space 3}log pseudolikelihood = {res:-488.29164}  
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}      1353
{txt}{col 51}Wald chi2({res}16{txt}){col 67}= {res}    118.49
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-488.29164{txt}{col 51}Pseudo R2{col 67}= {res}    0.2211

{txt}{hline 23}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 24}{c |}{col 36}    Robust
{col 1}           def_knowl19{col 24}{c |}      Coef.{col 36}   Std. Err.{col 48}      z{col 56}   P>|z|{col 64}     [95% Con{col 77}f. Interval]
{hline 23}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 9}partisan_9rev {c |}
{space 10}In-Partisan  {c |}{col 24}{res}{space 2}-1.266005{col 36}{space 2} .3237921{col 47}{space 1}   -3.91{col 56}{space 3}0.000{col 64}{space 4}-1.900626{col 77}{space 3}-.6313841
{txt}{space 2}disagree_total_close {c |}{col 24}{res}{space 2}-.0293739{col 36}{space 2} .0349341{col 47}{space 1}   -0.84{col 56}{space 3}0.400{col 64}{space 4}-.0978435{col 77}{space 3} .0390957
{txt}{space 22} {c |}
{space 9}partisan_9rev#{c |}
c.disagree_total_close {c |}
{space 10}In-Partisan  {c |}{col 24}{res}{space 2} .0714522{col 36}{space 2} .0395977{col 47}{space 1}    1.80{col 56}{space 3}0.071{col 64}{space 4}-.0061578{col 77}{space 3} .1490623
{txt}{space 22} {c |}
{space 13}numgiven1 {c |}{col 24}{res}{space 2}-.7313716{col 36}{space 2} .2870074{col 47}{space 1}   -2.55{col 56}{space 3}0.011{col 64}{space 4}-1.293896{col 77}{space 3}-.1688475
{txt}{space 6}network_interest {c |}{col 24}{res}{space 2}-.2654684{col 36}{space 2}  .144643{col 47}{space 1}   -1.84{col 56}{space 3}0.066{col 64}{space 4}-.5489635{col 77}{space 3} .0180267
{txt}{space 11}interest_w9 {c |}{col 24}{res}{space 2} .2218942{col 36}{space 2}  .143092{col 47}{space 1}    1.55{col 56}{space 3}0.121{col 64}{space 4} -.058561{col 77}{space 3} .5023493
{txt}{space 22} {c |}
{space 16}gender {c |}
{space 15}Female  {c |}{col 24}{res}{space 2}-.3765205{col 36}{space 2} .2472346{col 47}{space 1}   -1.52{col 56}{space 3}0.128{col 64}{space 4}-.8610914{col 77}{space 3} .1080504
{txt}{space 22} {c |}
{space 14}race_eth {c |}
{space 16}Black  {c |}{col 24}{res}{space 2}-1.392703{col 36}{space 2} .3322764{col 47}{space 1}   -4.19{col 56}{space 3}0.000{col 64}{space 4}-2.043953{col 77}{space 3}-.7414536
{txt}{space 13}Hispanic  {c |}{col 24}{res}{space 2}-.6082699{col 36}{space 2} .4400741{col 47}{space 1}   -1.38{col 56}{space 3}0.167{col 64}{space 4}-1.470799{col 77}{space 3} .2542595
{txt}{space 16}Other  {c |}{col 24}{res}{space 2}-.1681307{col 36}{space 2} .6146912{col 47}{space 1}   -0.27{col 56}{space 3}0.784{col 64}{space 4}-1.372903{col 77}{space 3} 1.036642
{txt}{space 22} {c |}
{space 15}marital {c |}
{space 14}Married  {c |}{col 24}{res}{space 2}-.1142681{col 36}{space 2} .2537716{col 47}{space 1}   -0.45{col 56}{space 3}0.653{col 64}{space 4}-.6116514{col 77}{space 3} .3831151
{txt}{space 19}age {c |}{col 24}{res}{space 2} .0216195{col 36}{space 2} .0073301{col 47}{space 1}    2.95{col 56}{space 3}0.003{col 64}{space 4} .0072527{col 77}{space 3} .0359863
{txt}{space 18}educ {c |}{col 24}{res}{space 2} .0413929{col 36}{space 2} .1297304{col 47}{space 1}    0.32{col 56}{space 3}0.750{col 64}{space 4} -.212874{col 77}{space 3} .2956598
{txt}{space 16}income {c |}{col 24}{res}{space 2}-.0155918{col 36}{space 2}  .034776{col 47}{space 1}   -0.45{col 56}{space 3}0.654{col 64}{space 4}-.0837515{col 77}{space 3} .0525679
{txt}{space 18}nfc1 {c |}{col 24}{res}{space 2} .1979072{col 36}{space 2} .1951101{col 47}{space 1}    1.01{col 56}{space 3}0.310{col 64}{space 4}-.1845017{col 77}{space 3}  .580316
{txt}{space 13}evaluate1 {c |}{col 24}{res}{space 2}-.0972115{col 36}{space 2} .3151265{col 47}{space 1}   -0.31{col 56}{space 3}0.758{col 64}{space 4} -.714848{col 77}{space 3} .5204251
{txt}{space 17}_cons {c |}{col 24}{res}{space 2}  4.21712{col 36}{space 2} 1.208831{col 47}{space 1}    3.49{col 56}{space 3}0.000{col 64}{space 4} 1.847855{col 77}{space 3} 6.586386
{txt}{hline 23}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-626.92863}  
Iteration 1:{space 3}log pseudolikelihood = {res:-504.24306}  
Iteration 2:{space 3}log pseudolikelihood = {res:-487.60583}  
Iteration 3:{space 3}log pseudolikelihood = {res:-487.17286}  
Iteration 4:{space 3}log pseudolikelihood = {res:-487.17171}  
Iteration 5:{space 3}log pseudolikelihood = {res:-487.17171}  
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}      1353
{txt}{col 51}Wald chi2({res}16{txt}){col 67}= {res}    123.29
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-487.17171{txt}{col 51}Pseudo R2{col 67}= {res}    0.2229

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}            def_knowl19{col 25}{c |}      Coef.{col 37}   Std. Err.{col 49}      z{col 57}   P>|z|{col 65}     [95% Con{col 78}f. Interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}partisan_9rev {c |}
{space 11}In-Partisan  {c |}{col 25}{res}{space 2}-1.125106{col 37}{space 2} .3678502{col 48}{space 1}   -3.06{col 57}{space 3}0.002{col 65}{space 4}-1.846079{col 78}{space 3}-.4041324
{txt}{space 2}disagree_total_weight {c |}{col 25}{res}{space 2}-.0260678{col 37}{space 2} .0387206{col 48}{space 1}   -0.67{col 57}{space 3}0.501{col 65}{space 4}-.1019587{col 78}{space 3} .0498232
{txt}{space 23} {c |}
{space 10}partisan_9rev#{c |}
c.disagree_total_weight {c |}
{space 11}In-Partisan  {c |}{col 25}{res}{space 2} .0821392{col 37}{space 2} .0443056{col 48}{space 1}    1.85{col 57}{space 3}0.064{col 65}{space 4}-.0046982{col 78}{space 3} .1689766
{txt}{space 23} {c |}
{space 14}numgiven1 {c |}{col 25}{res}{space 2}-.6821826{col 37}{space 2} .2829922{col 48}{space 1}   -2.41{col 57}{space 3}0.016{col 65}{space 4}-1.236837{col 78}{space 3}-.1275281
{txt}{space 7}network_interest {c |}{col 25}{res}{space 2}-.2572882{col 37}{space 2} .1448049{col 48}{space 1}   -1.78{col 57}{space 3}0.076{col 65}{space 4}-.5411006{col 78}{space 3} .0265242
{txt}{space 12}interest_w9 {c |}{col 25}{res}{space 2} .2134547{col 37}{space 2} .1436663{col 48}{space 1}    1.49{col 57}{space 3}0.137{col 65}{space 4}-.0681261{col 78}{space 3} .4950356
{txt}{space 23} {c |}
{space 17}gender {c |}
{space 16}Female  {c |}{col 25}{res}{space 2}-.3639011{col 37}{space 2} .2474981{col 48}{space 1}   -1.47{col 57}{space 3}0.141{col 65}{space 4}-.8489884{col 78}{space 3} .1211862
{txt}{space 23} {c |}
{space 15}race_eth {c |}
{space 17}Black  {c |}{col 25}{res}{space 2}-1.368714{col 37}{space 2} .3315361{col 48}{space 1}   -4.13{col 57}{space 3}0.000{col 65}{space 4}-2.018513{col 78}{space 3}-.7189152
{txt}{space 14}Hispanic  {c |}{col 25}{res}{space 2}-.6230635{col 37}{space 2}  .443611{col 48}{space 1}   -1.40{col 57}{space 3}0.160{col 65}{space 4}-1.492525{col 78}{space 3}  .246398
{txt}{space 17}Other  {c |}{col 25}{res}{space 2}-.1877333{col 37}{space 2} .6145449{col 48}{space 1}   -0.31{col 57}{space 3}0.760{col 65}{space 4}-1.392219{col 78}{space 3} 1.016753
{txt}{space 23} {c |}
{space 16}marital {c |}
{space 15}Married  {c |}{col 25}{res}{space 2}-.1093514{col 37}{space 2} .2549697{col 48}{space 1}   -0.43{col 57}{space 3}0.668{col 65}{space 4}-.6090829{col 78}{space 3} .3903801
{txt}{space 20}age {c |}{col 25}{res}{space 2} .0221997{col 37}{space 2} .0073727{col 48}{space 1}    3.01{col 57}{space 3}0.003{col 65}{space 4} .0077496{col 78}{space 3} .0366499
{txt}{space 19}educ {c |}{col 25}{res}{space 2} .0309308{col 37}{space 2} .1299035{col 48}{space 1}    0.24{col 57}{space 3}0.812{col 65}{space 4}-.2236753{col 78}{space 3} .2855369
{txt}{space 17}income {c |}{col 25}{res}{space 2}-.0139828{col 37}{space 2} .0347183{col 48}{space 1}   -0.40{col 57}{space 3}0.687{col 65}{space 4}-.0820295{col 78}{space 3} .0540639
{txt}{space 19}nfc1 {c |}{col 25}{res}{space 2} .2103143{col 37}{space 2} .1952706{col 48}{space 1}    1.08{col 57}{space 3}0.281{col 65}{space 4} -.172409{col 78}{space 3} .5930375
{txt}{space 14}evaluate1 {c |}{col 25}{res}{space 2}-.0671947{col 37}{space 2}  .312365{col 48}{space 1}   -0.22{col 57}{space 3}0.830{col 65}{space 4}-.6794188{col 78}{space 3} .5450293
{txt}{space 18}_cons {c |}{col 25}{res}{space 2} 3.981295{col 37}{space 2} 1.200963{col 48}{space 1}    3.32{col 57}{space 3}0.001{col 65}{space 4}  1.62745{col 78}{space 3}  6.33514
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est5{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-626.92863}  
Iteration 1:{space 3}log pseudolikelihood = {res:-509.53643}  
Iteration 2:{space 3}log pseudolikelihood = {res:-492.99076}  
Iteration 3:{space 3}log pseudolikelihood = {res:-492.54194}  
Iteration 4:{space 3}log pseudolikelihood = {res:-492.54066}  
Iteration 5:{space 3}log pseudolikelihood = {res:-492.54066}  
{res}
{txt}Logistic regression{col 51}Number of obs{col 67}= {res}      1353
{txt}{col 51}Wald chi2({res}16{txt}){col 67}= {res}    114.18
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-492.54066{txt}{col 51}Pseudo R2{col 67}= {res}    0.2144

{txt}{hline 24}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 25}{c |}{col 37}    Robust
{col 1}            def_knowl19{col 25}{c |}      Coef.{col 37}   Std. Err.{col 49}      z{col 57}   P>|z|{col 65}     [95% Con{col 78}f. Interval]
{hline 24}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}partisan_9rev {c |}
{space 11}In-Partisan  {c |}{col 25}{res}{space 2}-2.446161{col 37}{space 2}  .710537{col 48}{space 1}   -3.44{col 57}{space 3}0.001{col 65}{space 4}-3.838788{col 78}{space 3}-1.053534
{txt}{space 16}gendiff {c |}{col 25}{res}{space 2}-.1502152{col 37}{space 2} .2389723{col 48}{space 1}   -0.63{col 57}{space 3}0.530{col 65}{space 4}-.6185922{col 78}{space 3} .3181618
{txt}{space 23} {c |}
partisan_9rev#c.gendiff {c |}
{space 11}In-Partisan  {c |}{col 25}{res}{space 2} .3885481{col 37}{space 2} .3021107{col 48}{space 1}    1.29{col 57}{space 3}0.198{col 65}{space 4}-.2035781{col 78}{space 3} .9806742
{txt}{space 23} {c |}
{space 14}numgiven1 {c |}{col 25}{res}{space 2}-.7741903{col 37}{space 2} .2840097{col 48}{space 1}   -2.73{col 57}{space 3}0.006{col 65}{space 4}-1.330839{col 78}{space 3}-.2175414
{txt}{space 7}network_interest {c |}{col 25}{res}{space 2} -.270892{col 37}{space 2} .1445927{col 48}{space 1}   -1.87{col 57}{space 3}0.061{col 65}{space 4}-.5542885{col 78}{space 3} .0125045
{txt}{space 12}interest_w9 {c |}{col 25}{res}{space 2} .2284763{col 37}{space 2} .1456117{col 48}{space 1}    1.57{col 57}{space 3}0.117{col 65}{space 4}-.0569173{col 78}{space 3}   .51387
{txt}{space 23} {c |}
{space 17}gender {c |}
{space 16}Female  {c |}{col 25}{res}{space 2}-.3811653{col 37}{space 2} .2569066{col 48}{space 1}   -1.48{col 57}{space 3}0.138{col 65}{space 4}-.8846931{col 78}{space 3} .1223624
{txt}{space 23} {c |}
{space 15}race_eth {c |}
{space 17}Black  {c |}{col 25}{res}{space 2}-1.488559{col 37}{space 2} .3259849{col 48}{space 1}   -4.57{col 57}{space 3}0.000{col 65}{space 4}-2.127478{col 78}{space 3}-.8496404
{txt}{space 14}Hispanic  {c |}{col 25}{res}{space 2}-.6047876{col 37}{space 2} .4595237{col 48}{space 1}   -1.32{col 57}{space 3}0.188{col 65}{space 4}-1.505438{col 78}{space 3} .2958623
{txt}{space 17}Other  {c |}{col 25}{res}{space 2} .0300229{col 37}{space 2} .7080697{col 48}{space 1}    0.04{col 57}{space 3}0.966{col 65}{space 4}-1.357768{col 78}{space 3} 1.417814
{txt}{space 23} {c |}
{space 16}marital {c |}
{space 15}Married  {c |}{col 25}{res}{space 2}-.1233082{col 37}{space 2} .2540063{col 48}{space 1}   -0.49{col 57}{space 3}0.627{col 65}{space 4}-.6211513{col 78}{space 3}  .374535
{txt}{space 20}age {c |}{col 25}{res}{space 2} .0218834{col 37}{space 2} .0075246{col 48}{space 1}    2.91{col 57}{space 3}0.004{col 65}{space 4} .0071354{col 78}{space 3} .0366314
{txt}{space 19}educ {c |}{col 25}{res}{space 2} .0019358{col 37}{space 2} .1348038{col 48}{space 1}    0.01{col 57}{space 3}0.989{col 65}{space 4}-.2622749{col 78}{space 3} .2661465
{txt}{space 17}income {c |}{col 25}{res}{space 2}-.0104396{col 37}{space 2} .0355828{col 48}{space 1}   -0.29{col 57}{space 3}0.769{col 65}{space 4}-.0801807{col 78}{space 3} .0593015
{txt}{space 19}nfc1 {c |}{col 25}{res}{space 2} .2319633{col 37}{space 2} .1972448{col 48}{space 1}    1.18{col 57}{space 3}0.240{col 65}{space 4}-.1546294{col 78}{space 3}  .618556
{txt}{space 14}evaluate1 {c |}{col 25}{res}{space 2}-.1661883{col 37}{space 2} .3121847{col 48}{space 1}   -0.53{col 57}{space 3}0.594{col 65}{space 4} -.778059{col 78}{space 3} .4456824
{txt}{space 18}_cons {c |}{col 25}{res}{space 2} 4.868732{col 37}{space 2} 1.324136{col 48}{space 1}    3.68{col 57}{space 3}0.000{col 65}{space 4} 2.273473{col 78}{space 3} 7.463992
{txt}{hline 24}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est6{txt} stored)

{com}.                 
.                 
. esttab using 2008KNOWL_ALTMEASURES_JULY.rtf, onecell nobaselevels replace label pr2 aic bic se star(+ 0.10 * 0.05 ** 0.01) ///
>         mtitles("Original Measure" "(/D+A)" "Weigh: by Disc. Interest" ///
>                 "Weigh: by Tie Strength" "Weighted by Gen Dis" "Gen Disagree")  ///
>         title({c -(}\b Table XX.{c )-} "Alternative Measures of Disagreement - 2008-2009 ANES (KNOWLEDGE: JULY)") ///
>         rename(disagree_total Disagreement disagree_avg "Disagreement" disagree_total_int Disagreement disagree_total_close Disagreement ///
>                                 disagree_total_weight Disagreement gendiff Disagreement ///
>                                 1.partisan_9rev#c.disagree_total Partisan*Disagree ///
>                                 1.partisan_9rev#c.disagree_avg Partisan*Disagree ///
>                                 1.partisan_9rev#c.disagree_total_int Partisan*Disagree ///
>                                 1.partisan_9rev#c.disagree_total_close Partisan*Disagree ///
>                                 1.partisan_9rev#c.disagree_total_weight Partisan*Disagree ///
>                                 1.partisan_9rev#c.gendiff Partisan*Disagree)
{res}{txt}(output written to {browse  `"2008KNOWL_ALTMEASURES_JULY.rtf"'})

{com}. 
. /***Economic Evaluations***/
. 
. *W17
. 
. 
. eststo clear
{txt}
{com}. foreach var in disagree_total disagree_avg disagree_total_int disagree_total_close  ///
>         disagree_total_weight gendiff  {c -(}
{txt}  2{com}.         eststo: ologit w17_retro i.partisan_9rev##c.`var' c.numgiven network_interest interest_w9       ///
>         age educ income i.gender i.race i.empl_w11a i.marital nfc1 evaluate1 [pweight= WGTPP17]
{txt}  3{com}.         
.         {c )-}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1361.9834}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1305.9766}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1305.6336}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1305.6334}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}      1446
{txt}{col 51}Wald chi2({res}19{txt}){col 67}= {res}     62.09
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-1305.6334{txt}{col 51}Pseudo R2{col 67}= {res}    0.0414

{txt}{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}{col 44}    Robust
{col 1}                     w17_retro{col 32}{c |}      Coef.{col 44}   Std. Err.{col 56}      z{col 64}   P>|z|{col 72}     [95% Con{col 85}f. Interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}partisan_9rev {c |}
{space 18}In-Partisan  {c |}{col 32}{res}{space 2} .8022658{col 44}{space 2} .1666294{col 55}{space 1}    4.81{col 64}{space 3}0.000{col 72}{space 4} .4756781{col 85}{space 3} 1.128853
{txt}{space 16}disagree_total {c |}{col 32}{res}{space 2} .0342355{col 44}{space 2}  .053382{col 55}{space 1}    0.64{col 64}{space 3}0.521{col 72}{space 4}-.0703913{col 85}{space 3} .1388624
{txt}{space 30} {c |}
partisan_9rev#c.disagree_total {c |}
{space 18}In-Partisan  {c |}{col 32}{res}{space 2}-.0941759{col 44}{space 2} .0745971{col 55}{space 1}   -1.26{col 64}{space 3}0.207{col 72}{space 4}-.2403836{col 85}{space 3} .0520318
{txt}{space 30} {c |}
{space 22}numgiven {c |}{col 32}{res}{space 2} .0020384{col 44}{space 2} .0331001{col 55}{space 1}    0.06{col 64}{space 3}0.951{col 72}{space 4}-.0628367{col 85}{space 3} .0669134
{txt}{space 14}network_interest {c |}{col 32}{res}{space 2}-.1374712{col 44}{space 2} .0996323{col 55}{space 1}   -1.38{col 64}{space 3}0.168{col 72}{space 4}-.3327468{col 85}{space 3} .0578045
{txt}{space 19}interest_w9 {c |}{col 32}{res}{space 2} .0622975{col 44}{space 2} .0932316{col 55}{space 1}    0.67{col 64}{space 3}0.504{col 72}{space 4} -.120433{col 85}{space 3}  .245028
{txt}{space 27}age {c |}{col 32}{res}{space 2}-.0078931{col 44}{space 2} .0065819{col 55}{space 1}   -1.20{col 64}{space 3}0.230{col 72}{space 4}-.0207934{col 85}{space 3} .0050073
{txt}{space 26}educ {c |}{col 32}{res}{space 2} .1238663{col 44}{space 2} .0876615{col 55}{space 1}    1.41{col 64}{space 3}0.158{col 72}{space 4} -.047947{col 85}{space 3} .2956797
{txt}{space 24}income {c |}{col 32}{res}{space 2} .0668113{col 44}{space 2} .0228208{col 55}{space 1}    2.93{col 64}{space 3}0.003{col 72}{space 4} .0220833{col 85}{space 3} .1115392
{txt}{space 30} {c |}
{space 24}gender {c |}
{space 23}Female  {c |}{col 32}{res}{space 2}-.1501582{col 44}{space 2} .1632796{col 55}{space 1}   -0.92{col 64}{space 3}0.358{col 72}{space 4}-.4701804{col 85}{space 3} .1698639
{txt}{space 30} {c |}
{space 22}race_eth {c |}
{space 24}Black  {c |}{col 32}{res}{space 2} .1820779{col 44}{space 2} .2685885{col 55}{space 1}    0.68{col 64}{space 3}0.498{col 72}{space 4}-.3443459{col 85}{space 3} .7085017
{txt}{space 21}Hispanic  {c |}{col 32}{res}{space 2} .1310079{col 44}{space 2} .3141274{col 55}{space 1}    0.42{col 64}{space 3}0.677{col 72}{space 4}-.4846706{col 85}{space 3} .7466863
{txt}{space 24}Other  {c |}{col 32}{res}{space 2} .3122644{col 44}{space 2} .4775809{col 55}{space 1}    0.65{col 64}{space 3}0.513{col 72}{space 4}-.6237769{col 85}{space 3} 1.248306
{txt}{space 30} {c |}
{space 21}empl_w11a {c |}
{space 19}Unemployed  {c |}{col 32}{res}{space 2}-.5160788{col 44}{space 2} .4520902{col 55}{space 1}   -1.14{col 64}{space 3}0.254{col 72}{space 4}-1.402159{col 85}{space 3} .3700018
{txt}{space 22}Retired  {c |}{col 32}{res}{space 2} .3741241{col 44}{space 2} .2258977{col 55}{space 1}    1.66{col 64}{space 3}0.098{col 72}{space 4}-.0686273{col 85}{space 3} .8168755
{txt}{space 3}Disabled/Not Working/Other  {c |}{col 32}{res}{space 2} .5275505{col 44}{space 2} .2530737{col 55}{space 1}    2.08{col 64}{space 3}0.037{col 72}{space 4} .0315351{col 85}{space 3} 1.023566
{txt}{space 30} {c |}
{space 23}marital {c |}
{space 22}Married  {c |}{col 32}{res}{space 2}-.1587798{col 44}{space 2} .1727091{col 55}{space 1}   -0.92{col 64}{space 3}0.358{col 72}{space 4}-.4972835{col 85}{space 3} .1797238
{txt}{space 26}nfc1 {c |}{col 32}{res}{space 2} .0626554{col 44}{space 2} .1194464{col 55}{space 1}    0.52{col 64}{space 3}0.600{col 72}{space 4}-.1714553{col 85}{space 3} .2967661
{txt}{space 21}evaluate1 {c |}{col 32}{res}{space 2} .1877123{col 44}{space 2} .2015773{col 55}{space 1}    0.93{col 64}{space 3}0.352{col 72}{space 4} -.207372{col 85}{space 3} .5827966
{txt}{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                         /cut1 {c |}{col 32}{res}{space 2} 1.329654{col 44}{space 2} .5554272{col 72}{space 4} .2410371{col 85}{space 3} 2.418272
{txt}                         /cut2 {c |}{col 32}{res}{space 2} 2.966365{col 44}{space 2} .5517034{col 72}{space 4} 1.885047{col 85}{space 3} 4.047684
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1361.9834}  
Iteration 1:{space 3}log pseudolikelihood = {res: -1306.339}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1305.9965}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1305.9964}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}      1446
{txt}{col 51}Wald chi2({res}19{txt}){col 67}= {res}     61.63
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-1305.9964{txt}{col 51}Pseudo R2{col 67}= {res}    0.0411

{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                   w17_retro{col 30}{c |}      Coef.{col 42}   Std. Err.{col 54}      z{col 62}   P>|z|{col 70}     [95% Con{col 83}f. Interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}partisan_9rev {c |}
{space 16}In-Partisan  {c |}{col 30}{res}{space 2} .8171779{col 42}{space 2} .1638342{col 53}{space 1}    4.99{col 62}{space 3}0.000{col 70}{space 4} .4960688{col 83}{space 3} 1.138287
{txt}{space 16}disagree_avg {c |}{col 30}{res}{space 2} .1091018{col 42}{space 2} .1525358{col 53}{space 1}    0.72{col 62}{space 3}0.474{col 70}{space 4}-.1898629{col 83}{space 3} .4080665
{txt}{space 28} {c |}
partisan_9rev#c.disagree_avg {c |}
{space 16}In-Partisan  {c |}{col 30}{res}{space 2}-.2374148{col 42}{space 2} .2067834{col 53}{space 1}   -1.15{col 62}{space 3}0.251{col 70}{space 4}-.6427028{col 83}{space 3} .1678733
{txt}{space 28} {c |}
{space 20}numgiven {c |}{col 30}{res}{space 2} .0033518{col 42}{space 2} .0331977{col 53}{space 1}    0.10{col 62}{space 3}0.920{col 70}{space 4}-.0617146{col 83}{space 3} .0684181
{txt}{space 12}network_interest {c |}{col 30}{res}{space 2}-.1340578{col 42}{space 2} .0997033{col 53}{space 1}   -1.34{col 62}{space 3}0.179{col 70}{space 4}-.3294727{col 83}{space 3} .0613571
{txt}{space 17}interest_w9 {c |}{col 30}{res}{space 2} .0594944{col 42}{space 2} .0930641{col 53}{space 1}    0.64{col 62}{space 3}0.523{col 70}{space 4}-.1229079{col 83}{space 3} .2418967
{txt}{space 25}age {c |}{col 30}{res}{space 2}-.0079034{col 42}{space 2} .0065746{col 53}{space 1}   -1.20{col 62}{space 3}0.229{col 70}{space 4}-.0207894{col 83}{space 3} .0049825
{txt}{space 24}educ {c |}{col 30}{res}{space 2} .1255818{col 42}{space 2} .0878472{col 53}{space 1}    1.43{col 62}{space 3}0.153{col 70}{space 4}-.0465956{col 83}{space 3} .2977591
{txt}{space 22}income {c |}{col 30}{res}{space 2} .0670984{col 42}{space 2} .0227431{col 53}{space 1}    2.95{col 62}{space 3}0.003{col 70}{space 4} .0225226{col 83}{space 3} .1116741
{txt}{space 28} {c |}
{space 22}gender {c |}
{space 21}Female  {c |}{col 30}{res}{space 2}-.1475438{col 42}{space 2} .1630982{col 53}{space 1}   -0.90{col 62}{space 3}0.366{col 70}{space 4}-.4672105{col 83}{space 3} .1721228
{txt}{space 28} {c |}
{space 20}race_eth {c |}
{space 22}Black  {c |}{col 30}{res}{space 2} .1966184{col 42}{space 2} .2662924{col 53}{space 1}    0.74{col 62}{space 3}0.460{col 70}{space 4}-.3253052{col 83}{space 3}  .718542
{txt}{space 19}Hispanic  {c |}{col 30}{res}{space 2} .1257584{col 42}{space 2} .3128948{col 53}{space 1}    0.40{col 62}{space 3}0.688{col 70}{space 4}-.4875041{col 83}{space 3} .7390208
{txt}{space 22}Other  {c |}{col 30}{res}{space 2} .2967914{col 42}{space 2} .4794771{col 53}{space 1}    0.62{col 62}{space 3}0.536{col 70}{space 4}-.6429665{col 83}{space 3} 1.236549
{txt}{space 28} {c |}
{space 19}empl_w11a {c |}
{space 17}Unemployed  {c |}{col 30}{res}{space 2}-.5191335{col 42}{space 2} .4523725{col 53}{space 1}   -1.15{col 62}{space 3}0.251{col 70}{space 4}-1.405767{col 83}{space 3} .3675002
{txt}{space 20}Retired  {c |}{col 30}{res}{space 2} .3757357{col 42}{space 2} .2259552{col 53}{space 1}    1.66{col 62}{space 3}0.096{col 70}{space 4}-.0671284{col 83}{space 3} .8185997
{txt}{space 1}Disabled/Not Working/Other  {c |}{col 30}{res}{space 2} .5306094{col 42}{space 2} .2531997{col 53}{space 1}    2.10{col 62}{space 3}0.036{col 70}{space 4}  .034347{col 83}{space 3} 1.026872
{txt}{space 28} {c |}
{space 21}marital {c |}
{space 20}Married  {c |}{col 30}{res}{space 2}-.1600888{col 42}{space 2} .1726854{col 53}{space 1}   -0.93{col 62}{space 3}0.354{col 70}{space 4} -.498546{col 83}{space 3} .1783685
{txt}{space 24}nfc1 {c |}{col 30}{res}{space 2} .0592442{col 42}{space 2} .1194887{col 53}{space 1}    0.50{col 62}{space 3}0.620{col 70}{space 4}-.1749493{col 83}{space 3} .2934378
{txt}{space 19}evaluate1 {c |}{col 30}{res}{space 2} .1979518{col 42}{space 2} .2011231{col 53}{space 1}    0.98{col 62}{space 3}0.325{col 70}{space 4}-.1962421{col 83}{space 3} .5921458
{txt}{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                       /cut1 {c |}{col 30}{res}{space 2} 1.355405{col 42}{space 2} .5557397{col 70}{space 4} .2661749{col 83}{space 3} 2.444635
{txt}                       /cut2 {c |}{col 30}{res}{space 2} 2.991451{col 42}{space 2} .5524303{col 70}{space 4} 1.908708{col 83}{space 3} 4.074195
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1361.9834}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1304.8063}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1304.4451}  
Iteration 3:{space 3}log pseudolikelihood = {res: -1304.445}  
Iteration 4:{space 3}log pseudolikelihood = {res: -1304.445}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}      1446
{txt}{col 51}Wald chi2({res}19{txt}){col 67}= {res}     64.27
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res} -1304.445{txt}{col 51}Pseudo R2{col 67}= {res}    0.0422

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}                         w17_retro{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      z{col 68}   P>|z|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}partisan_9rev {c |}
{space 22}In-Partisan  {c |}{col 36}{res}{space 2} .7681517{col 48}{space 2} .1680207{col 59}{space 1}    4.57{col 68}{space 3}0.000{col 76}{space 4} .4388372{col 89}{space 3} 1.097466
{txt}{space 16}disagree_total_int {c |}{col 36}{res}{space 2} .0112203{col 48}{space 2} .0131835{col 59}{space 1}    0.85{col 68}{space 3}0.395{col 76}{space 4}-.0146189{col 89}{space 3} .0370595
{txt}{space 34} {c |}
partisan_9rev#c.disagree_total_int {c |}
{space 22}In-Partisan  {c |}{col 36}{res}{space 2}-.0321939{col 48}{space 2}  .018528{col 59}{space 1}   -1.74{col 68}{space 3}0.082{col 76}{space 4}-.0685081{col 89}{space 3} .0041203
{txt}{space 34} {c |}
{space 26}numgiven {c |}{col 36}{res}{space 2}  .001692{col 48}{space 2}  .033175{col 59}{space 1}    0.05{col 68}{space 3}0.959{col 76}{space 4}-.0633298{col 89}{space 3} .0667139
{txt}{space 18}network_interest {c |}{col 36}{res}{space 2} -.147062{col 48}{space 2} .1000466{col 59}{space 1}   -1.47{col 68}{space 3}0.142{col 76}{space 4}-.3431497{col 89}{space 3} .0490257
{txt}{space 23}interest_w9 {c |}{col 36}{res}{space 2} .0598824{col 48}{space 2} .0928102{col 59}{space 1}    0.65{col 68}{space 3}0.519{col 76}{space 4}-.1220222{col 89}{space 3} .2417871
{txt}{space 31}age {c |}{col 36}{res}{space 2}-.0079967{col 48}{space 2}  .006567{col 59}{space 1}   -1.22{col 68}{space 3}0.223{col 76}{space 4}-.0208678{col 89}{space 3} .0048743
{txt}{space 30}educ {c |}{col 36}{res}{space 2} .1229393{col 48}{space 2} .0876284{col 59}{space 1}    1.40{col 68}{space 3}0.161{col 76}{space 4}-.0488092{col 89}{space 3} .2946879
{txt}{space 28}income {c |}{col 36}{res}{space 2} .0663929{col 48}{space 2} .0227108{col 59}{space 1}    2.92{col 68}{space 3}0.003{col 76}{space 4} .0218806{col 89}{space 3} .1109051
{txt}{space 34} {c |}
{space 28}gender {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.1515594{col 48}{space 2} .1628591{col 59}{space 1}   -0.93{col 68}{space 3}0.352{col 76}{space 4}-.4707574{col 89}{space 3} .1676386
{txt}{space 34} {c |}
{space 26}race_eth {c |}
{space 28}Black  {c |}{col 36}{res}{space 2} .1557999{col 48}{space 2}   .27006{col 59}{space 1}    0.58{col 68}{space 3}0.564{col 76}{space 4}-.3735079{col 89}{space 3} .6851076
{txt}{space 25}Hispanic  {c |}{col 36}{res}{space 2} .1405642{col 48}{space 2} .3143507{col 59}{space 1}    0.45{col 68}{space 3}0.655{col 76}{space 4}-.4755517{col 89}{space 3} .7566802
{txt}{space 28}Other  {c |}{col 36}{res}{space 2} .3095865{col 48}{space 2} .4727244{col 59}{space 1}    0.65{col 68}{space 3}0.513{col 76}{space 4}-.6169363{col 89}{space 3} 1.236109
{txt}{space 34} {c |}
{space 25}empl_w11a {c |}
{space 23}Unemployed  {c |}{col 36}{res}{space 2} -.502036{col 48}{space 2} .4531679{col 59}{space 1}   -1.11{col 68}{space 3}0.268{col 76}{space 4}-1.390229{col 89}{space 3} .3861567
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2} .3748751{col 48}{space 2} .2256637{col 59}{space 1}    1.66{col 68}{space 3}0.097{col 76}{space 4}-.0674177{col 89}{space 3} .8171678
{txt}{space 7}Disabled/Not Working/Other  {c |}{col 36}{res}{space 2} .5249148{col 48}{space 2} .2540408{col 59}{space 1}    2.07{col 68}{space 3}0.039{col 76}{space 4} .0270039{col 89}{space 3} 1.022826
{txt}{space 34} {c |}
{space 27}marital {c |}
{space 26}Married  {c |}{col 36}{res}{space 2}-.1587132{col 48}{space 2} .1730793{col 59}{space 1}   -0.92{col 68}{space 3}0.359{col 76}{space 4}-.4979423{col 89}{space 3}  .180516
{txt}{space 30}nfc1 {c |}{col 36}{res}{space 2} .0619224{col 48}{space 2}  .119741{col 59}{space 1}    0.52{col 68}{space 3}0.605{col 76}{space 4}-.1727657{col 89}{space 3} .2966104
{txt}{space 25}evaluate1 {c |}{col 36}{res}{space 2} .1831801{col 48}{space 2} .2004835{col 59}{space 1}    0.91{col 68}{space 3}0.361{col 76}{space 4}-.2097603{col 89}{space 3} .5761205
{txt}{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                             /cut1 {c |}{col 36}{res}{space 2} 1.252234{col 48}{space 2} .5629799{col 76}{space 4} .1488138{col 89}{space 3} 2.355654
{txt}                             /cut2 {c |}{col 36}{res}{space 2} 2.891276{col 48}{space 2} .5590458{col 76}{space 4} 1.795567{col 89}{space 3} 3.986986
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1361.9834}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1304.7702}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1304.4048}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1304.4047}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1304.4047}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}      1446
{txt}{col 51}Wald chi2({res}19{txt}){col 67}= {res}     63.67
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-1304.4047{txt}{col 51}Pseudo R2{col 67}= {res}    0.0423

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}                  w17_retro{col 29}{c |}      Coef.{col 41}   Std. Err.{col 53}      z{col 61}   P>|z|{col 69}     [95% Con{col 82}f. Interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}partisan_9rev {c |}
{space 15}In-Partisan  {c |}{col 29}{res}{space 2} .7608166{col 41}{space 2} .1683071{col 52}{space 1}    4.52{col 61}{space 3}0.000{col 69}{space 4} .4309406{col 82}{space 3} 1.090693
{txt}{space 7}disagree_total_close {c |}{col 29}{res}{space 2} .0121801{col 41}{space 2} .0128714{col 52}{space 1}    0.95{col 61}{space 3}0.344{col 69}{space 4}-.0130473{col 82}{space 3} .0374076
{txt}{space 27} {c |}
{space 14}partisan_9rev#{c |}
{space 5}c.disagree_total_close {c |}
{space 15}In-Partisan  {c |}{col 29}{res}{space 2}-.0313473{col 41}{space 2} .0179572{col 52}{space 1}   -1.75{col 61}{space 3}0.081{col 69}{space 4}-.0665428{col 82}{space 3} .0038482
{txt}{space 27} {c |}
{space 19}numgiven {c |}{col 29}{res}{space 2} .0017013{col 41}{space 2}  .033095{col 52}{space 1}    0.05{col 61}{space 3}0.959{col 69}{space 4}-.0631638{col 82}{space 3} .0665663
{txt}{space 11}network_interest {c |}{col 29}{res}{space 2}-.1382349{col 41}{space 2} .0991249{col 52}{space 1}   -1.39{col 61}{space 3}0.163{col 69}{space 4}-.3325162{col 82}{space 3} .0560464
{txt}{space 16}interest_w9 {c |}{col 29}{res}{space 2} .0621689{col 41}{space 2} .0924931{col 52}{space 1}    0.67{col 61}{space 3}0.501{col 69}{space 4}-.1191143{col 82}{space 3} .2434522
{txt}{space 24}age {c |}{col 29}{res}{space 2}-.0080504{col 41}{space 2} .0065615{col 52}{space 1}   -1.23{col 61}{space 3}0.220{col 69}{space 4}-.0209107{col 82}{space 3} .0048099
{txt}{space 23}educ {c |}{col 29}{res}{space 2} .1262605{col 41}{space 2} .0871212{col 52}{space 1}    1.45{col 61}{space 3}0.147{col 69}{space 4}-.0444938{col 82}{space 3} .2970149
{txt}{space 21}income {c |}{col 29}{res}{space 2} .0664777{col 41}{space 2} .0226918{col 52}{space 1}    2.93{col 61}{space 3}0.003{col 69}{space 4} .0220025{col 82}{space 3} .1109529
{txt}{space 27} {c |}
{space 21}gender {c |}
{space 20}Female  {c |}{col 29}{res}{space 2}-.1459788{col 41}{space 2} .1632816{col 52}{space 1}   -0.89{col 61}{space 3}0.371{col 69}{space 4}-.4660049{col 82}{space 3} .1740472
{txt}{space 27} {c |}
{space 19}race_eth {c |}
{space 21}Black  {c |}{col 29}{res}{space 2} .1640612{col 41}{space 2} .2703493{col 52}{space 1}    0.61{col 61}{space 3}0.544{col 69}{space 4}-.3658137{col 82}{space 3} .6939361
{txt}{space 18}Hispanic  {c |}{col 29}{res}{space 2} .1303629{col 41}{space 2}  .313015{col 52}{space 1}    0.42{col 61}{space 3}0.677{col 69}{space 4}-.4831352{col 82}{space 3} .7438611
{txt}{space 21}Other  {c |}{col 29}{res}{space 2}  .304902{col 41}{space 2}   .47223{col 52}{space 1}    0.65{col 61}{space 3}0.518{col 69}{space 4}-.6206519{col 82}{space 3} 1.230456
{txt}{space 27} {c |}
{space 18}empl_w11a {c |}
{space 16}Unemployed  {c |}{col 29}{res}{space 2}-.5108542{col 41}{space 2} .4523511{col 52}{space 1}   -1.13{col 61}{space 3}0.259{col 69}{space 4}-1.397446{col 82}{space 3} .3757377
{txt}{space 19}Retired  {c |}{col 29}{res}{space 2} .3705795{col 41}{space 2} .2244976{col 52}{space 1}    1.65{col 61}{space 3}0.099{col 69}{space 4}-.0694277{col 82}{space 3} .8105868
{txt}Disabled/Not Working/Other  {c |}{col 29}{res}{space 2} .5252519{col 41}{space 2} .2520396{col 52}{space 1}    2.08{col 61}{space 3}0.037{col 69}{space 4} .0312633{col 82}{space 3}  1.01924
{txt}{space 27} {c |}
{space 20}marital {c |}
{space 19}Married  {c |}{col 29}{res}{space 2}-.1549944{col 41}{space 2} .1727077{col 52}{space 1}   -0.90{col 61}{space 3}0.369{col 69}{space 4}-.4934952{col 82}{space 3} .1835065
{txt}{space 23}nfc1 {c |}{col 29}{res}{space 2} .0629009{col 41}{space 2} .1193043{col 52}{space 1}    0.53{col 61}{space 3}0.598{col 69}{space 4}-.1709313{col 82}{space 3}  .296733
{txt}{space 18}evaluate1 {c |}{col 29}{res}{space 2} .1839053{col 41}{space 2} .2013419{col 52}{space 1}    0.91{col 61}{space 3}0.361{col 69}{space 4}-.2107176{col 82}{space 3} .5785282
{txt}{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                      /cut1 {c |}{col 29}{res}{space 2} 1.300157{col 41}{space 2} .5557433{col 69}{space 4} .2109203{col 82}{space 3} 2.389394
{txt}                      /cut2 {c |}{col 29}{res}{space 2} 2.939278{col 41}{space 2} .5518913{col 69}{space 4} 1.857591{col 82}{space 3} 4.020965
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1361.9834}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1305.2789}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1304.9188}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1304.9187}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1304.9187}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}      1446
{txt}{col 51}Wald chi2({res}19{txt}){col 67}= {res}     63.72
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-1304.9187{txt}{col 51}Pseudo R2{col 67}= {res}    0.0419

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}                  w17_retro{col 29}{c |}      Coef.{col 41}   Std. Err.{col 53}      z{col 61}   P>|z|{col 69}     [95% Con{col 82}f. Interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}partisan_9rev {c |}
{space 15}In-Partisan  {c |}{col 29}{res}{space 2} .7272599{col 41}{space 2} .1794534{col 52}{space 1}    4.05{col 61}{space 3}0.000{col 69}{space 4} .3755376{col 82}{space 3} 1.078982
{txt}{space 6}disagree_total_weight {c |}{col 29}{res}{space 2} .0153471{col 41}{space 2} .0142794{col 52}{space 1}    1.07{col 61}{space 3}0.282{col 69}{space 4}  -.01264{col 82}{space 3} .0433342
{txt}{space 27} {c |}
{space 14}partisan_9rev#{c |}
{space 4}c.disagree_total_weight {c |}
{space 15}In-Partisan  {c |}{col 29}{res}{space 2}-.0329306{col 41}{space 2} .0196003{col 52}{space 1}   -1.68{col 61}{space 3}0.093{col 69}{space 4}-.0713464{col 82}{space 3} .0054852
{txt}{space 27} {c |}
{space 19}numgiven {c |}{col 29}{res}{space 2}  .001741{col 41}{space 2} .0331507{col 52}{space 1}    0.05{col 61}{space 3}0.958{col 69}{space 4}-.0632333{col 82}{space 3} .0667152
{txt}{space 11}network_interest {c |}{col 29}{res}{space 2}-.1340768{col 41}{space 2} .0990326{col 52}{space 1}   -1.35{col 61}{space 3}0.176{col 69}{space 4}-.3281771{col 82}{space 3} .0600234
{txt}{space 16}interest_w9 {c |}{col 29}{res}{space 2} .0606439{col 41}{space 2} .0924076{col 52}{space 1}    0.66{col 61}{space 3}0.512{col 69}{space 4}-.1204717{col 82}{space 3} .2417595
{txt}{space 24}age {c |}{col 29}{res}{space 2}-.0081894{col 41}{space 2} .0065924{col 52}{space 1}   -1.24{col 61}{space 3}0.214{col 69}{space 4}-.0211103{col 82}{space 3} .0047315
{txt}{space 23}educ {c |}{col 29}{res}{space 2}  .129804{col 41}{space 2} .0879414{col 52}{space 1}    1.48{col 61}{space 3}0.140{col 69}{space 4} -.042558{col 82}{space 3}  .302166
{txt}{space 21}income {c |}{col 29}{res}{space 2} .0656233{col 41}{space 2} .0227769{col 52}{space 1}    2.88{col 61}{space 3}0.004{col 69}{space 4} .0209813{col 82}{space 3} .1102652
{txt}{space 27} {c |}
{space 21}gender {c |}
{space 20}Female  {c |}{col 29}{res}{space 2}-.1446237{col 41}{space 2} .1631961{col 52}{space 1}   -0.89{col 61}{space 3}0.376{col 69}{space 4}-.4644822{col 82}{space 3} .1752347
{txt}{space 27} {c |}
{space 19}race_eth {c |}
{space 21}Black  {c |}{col 29}{res}{space 2} .1745817{col 41}{space 2} .2674023{col 52}{space 1}    0.65{col 61}{space 3}0.514{col 69}{space 4}-.3495172{col 82}{space 3} .6986806
{txt}{space 18}Hispanic  {c |}{col 29}{res}{space 2} .1237649{col 41}{space 2} .3139201{col 52}{space 1}    0.39{col 61}{space 3}0.693{col 69}{space 4}-.4915071{col 82}{space 3}  .739037
{txt}{space 21}Other  {c |}{col 29}{res}{space 2} .3026261{col 41}{space 2} .4803515{col 52}{space 1}    0.63{col 61}{space 3}0.529{col 69}{space 4}-.6388456{col 82}{space 3} 1.244098
{txt}{space 27} {c |}
{space 18}empl_w11a {c |}
{space 16}Unemployed  {c |}{col 29}{res}{space 2}-.5223764{col 41}{space 2} .4514305{col 52}{space 1}   -1.16{col 61}{space 3}0.247{col 69}{space 4}-1.407164{col 82}{space 3} .3624111
{txt}{space 19}Retired  {c |}{col 29}{res}{space 2} .3744986{col 41}{space 2} .2256265{col 52}{space 1}    1.66{col 61}{space 3}0.097{col 69}{space 4}-.0677212{col 82}{space 3} .8167184
{txt}Disabled/Not Working/Other  {c |}{col 29}{res}{space 2} .5330818{col 41}{space 2} .2539362{col 52}{space 1}    2.10{col 61}{space 3}0.036{col 69}{space 4} .0353759{col 82}{space 3} 1.030788
{txt}{space 27} {c |}
{space 20}marital {c |}
{space 19}Married  {c |}{col 29}{res}{space 2}-.1548837{col 41}{space 2} .1729318{col 52}{space 1}   -0.90{col 61}{space 3}0.370{col 69}{space 4}-.4938238{col 82}{space 3} .1840563
{txt}{space 23}nfc1 {c |}{col 29}{res}{space 2} .0627148{col 41}{space 2} .1195066{col 52}{space 1}    0.52{col 61}{space 3}0.600{col 69}{space 4}-.1715139{col 82}{space 3} .2969435
{txt}{space 18}evaluate1 {c |}{col 29}{res}{space 2} .1856954{col 41}{space 2} .2019015{col 52}{space 1}    0.92{col 61}{space 3}0.358{col 69}{space 4}-.2100243{col 82}{space 3} .5814151
{txt}{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                      /cut1 {c |}{col 29}{res}{space 2} 1.280896{col 41}{space 2} .5559945{col 69}{space 4} .1911665{col 82}{space 3} 2.370625
{txt}                      /cut2 {c |}{col 29}{res}{space 2} 2.918713{col 41}{space 2}  .552221{col 69}{space 4}  1.83638{col 82}{space 3} 4.001046
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est5{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1361.9834}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1304.2821}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1303.8752}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1303.8751}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1303.8751}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}      1446
{txt}{col 51}Wald chi2({res}19{txt}){col 67}= {res}     65.57
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-1303.8751{txt}{col 51}Pseudo R2{col 67}= {res}    0.0427

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}                  w17_retro{col 29}{c |}      Coef.{col 41}   Std. Err.{col 53}      z{col 61}   P>|z|{col 69}     [95% Con{col 82}f. Interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}partisan_9rev {c |}
{space 15}In-Partisan  {c |}{col 29}{res}{space 2} 1.634144{col 41}{space 2} .4253179{col 52}{space 1}    3.84{col 61}{space 3}0.000{col 69}{space 4} .8005363{col 82}{space 3} 2.467752
{txt}{space 20}gendiff {c |}{col 29}{res}{space 2} .2394921{col 41}{space 2} .1270446{col 52}{space 1}    1.89{col 61}{space 3}0.059{col 69}{space 4}-.0095108{col 82}{space 3} .4884949
{txt}{space 27} {c |}
{space 4}partisan_9rev#c.gendiff {c |}
{space 15}In-Partisan  {c |}{col 29}{res}{space 2}-.3388999{col 41}{space 2} .1754478{col 52}{space 1}   -1.93{col 61}{space 3}0.053{col 69}{space 4}-.6827713{col 82}{space 3} .0049714
{txt}{space 27} {c |}
{space 19}numgiven {c |}{col 29}{res}{space 2} .0017122{col 41}{space 2} .0328513{col 52}{space 1}    0.05{col 61}{space 3}0.958{col 69}{space 4}-.0626752{col 82}{space 3} .0660996
{txt}{space 11}network_interest {c |}{col 29}{res}{space 2}-.1322818{col 41}{space 2} .0979302{col 52}{space 1}   -1.35{col 61}{space 3}0.177{col 69}{space 4}-.3242215{col 82}{space 3} .0596579
{txt}{space 16}interest_w9 {c |}{col 29}{res}{space 2} .0583078{col 41}{space 2} .0925383{col 52}{space 1}    0.63{col 61}{space 3}0.529{col 69}{space 4}-.1230639{col 82}{space 3} .2396796
{txt}{space 24}age {c |}{col 29}{res}{space 2}-.0088753{col 41}{space 2}  .006683{col 52}{space 1}   -1.33{col 61}{space 3}0.184{col 69}{space 4}-.0219737{col 82}{space 3} .0042231
{txt}{space 23}educ {c |}{col 29}{res}{space 2} .1345638{col 41}{space 2} .0865629{col 52}{space 1}    1.55{col 61}{space 3}0.120{col 69}{space 4}-.0350965{col 82}{space 3}  .304224
{txt}{space 21}income {c |}{col 29}{res}{space 2} .0645337{col 41}{space 2} .0226252{col 52}{space 1}    2.85{col 61}{space 3}0.004{col 69}{space 4} .0201891{col 82}{space 3} .1088783
{txt}{space 27} {c |}
{space 21}gender {c |}
{space 20}Female  {c |}{col 29}{res}{space 2}-.1440046{col 41}{space 2} .1633637{col 52}{space 1}   -0.88{col 61}{space 3}0.378{col 69}{space 4}-.4641915{col 82}{space 3} .1761823
{txt}{space 27} {c |}
{space 19}race_eth {c |}
{space 21}Black  {c |}{col 29}{res}{space 2} .2225184{col 41}{space 2} .2599439{col 52}{space 1}    0.86{col 61}{space 3}0.392{col 69}{space 4}-.2869622{col 82}{space 3}  .731999
{txt}{space 18}Hispanic  {c |}{col 29}{res}{space 2} .1123001{col 41}{space 2}  .315026{col 52}{space 1}    0.36{col 61}{space 3}0.721{col 69}{space 4}-.5051396{col 82}{space 3} .7297397
{txt}{space 21}Other  {c |}{col 29}{res}{space 2}  .283763{col 41}{space 2} .5024192{col 52}{space 1}    0.56{col 61}{space 3}0.572{col 69}{space 4}-.7009605{col 82}{space 3} 1.268486
{txt}{space 27} {c |}
{space 18}empl_w11a {c |}
{space 16}Unemployed  {c |}{col 29}{res}{space 2}-.5250767{col 41}{space 2} .4473604{col 52}{space 1}   -1.17{col 61}{space 3}0.241{col 69}{space 4}-1.401887{col 82}{space 3} .3517336
{txt}{space 19}Retired  {c |}{col 29}{res}{space 2} .3851847{col 41}{space 2} .2266963{col 52}{space 1}    1.70{col 61}{space 3}0.089{col 69}{space 4}-.0591318{col 82}{space 3} .8295013
{txt}Disabled/Not Working/Other  {c |}{col 29}{res}{space 2} .5368275{col 41}{space 2} .2516117{col 52}{space 1}    2.13{col 61}{space 3}0.033{col 69}{space 4} .0436777{col 82}{space 3} 1.029977
{txt}{space 27} {c |}
{space 20}marital {c |}
{space 19}Married  {c |}{col 29}{res}{space 2}-.1562828{col 41}{space 2}  .173795{col 52}{space 1}   -0.90{col 61}{space 3}0.369{col 69}{space 4}-.4969147{col 82}{space 3} .1843492
{txt}{space 23}nfc1 {c |}{col 29}{res}{space 2} .0526067{col 41}{space 2} .1191494{col 52}{space 1}    0.44{col 61}{space 3}0.659{col 69}{space 4}-.1809218{col 82}{space 3} .2861351
{txt}{space 18}evaluate1 {c |}{col 29}{res}{space 2} .2066579{col 41}{space 2} .2018892{col 52}{space 1}    1.02{col 61}{space 3}0.306{col 69}{space 4}-.1890376{col 82}{space 3} .6023534
{txt}{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                      /cut1 {c |}{col 29}{res}{space 2} 1.857326{col 41}{space 2} .6351292{col 69}{space 4} .6124961{col 82}{space 3} 3.102157
{txt}                      /cut2 {c |}{col 29}{res}{space 2} 3.496074{col 41}{space 2} .6275812{col 69}{space 4} 2.266038{col 82}{space 3} 4.726111
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est6{txt} stored)

{com}.         
. esttab using 2008ECON_ALTMEASURES_MAY.rtf, onecell nobaselevels replace label pr2 aic bic se star(+ 0.10 * 0.05 ** 0.01) ///
>         mtitles("Original Measure" "(/D+A)" "Weigh: by Disc. Interest" ///
>                 "Weigh: by Tie Strength" "Weighted by Gen Dis" "Gen Disagree")  ///
>         title({c -(}\b Table XX.{c )-} "Alternative Measures of Disagreement - 2008-2009 ANES (ECON MAY)") ///
>         rename(disagree_total Disagreement disagree_avg "Disagreement" disagree_total_int Disagreement disagree_total_close Disagreement ///
>                                 disagree_total_weight Disagreement gendiff Disagreement ///
>                                 1.partisan_9rev#c.disagree_total Partisan*Disagree ///
>                                 1.partisan_9rev#c.disagree_avg Partisan*Disagree ///
>                                 1.partisan_9rev#c.disagree_total_int Partisan*Disagree ///
>                                 1.partisan_9rev#c.disagree_total_close Partisan*Disagree ///
>                                 1.partisan_9rev#c.disagree_total_weight Partisan*Disagree ///
>                                 1.partisan_9rev#c.gendiff Partisan*Disagree)
{res}{txt}(output written to {browse  `"2008ECON_ALTMEASURES_MAY.rtf"'})

{com}.         
.         
. *W19
. 
. eststo clear
{txt}
{com}. foreach var in disagree_total disagree_avg disagree_total_int disagree_total_close  ///
>         disagree_total_weight gendiff  {c -(}
{txt}  2{com}.         eststo: ologit w19_retro i.partisan_9rev##c.`var' c.numgiven network_interest interest_w9 ///
>                 age educ income i.gender i.race i.empl_w11a i.marital nfc1 evaluate1 [pweight= WGTPP19]
{txt}  3{com}.         {c )-}

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1173.9004}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1081.5697}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1080.0206}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1080.0177}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1080.0177}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}      1352
{txt}{col 51}Wald chi2({res}19{txt}){col 67}= {res}    112.83
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-1080.0177{txt}{col 51}Pseudo R2{col 67}= {res}    0.0800

{txt}{hline 31}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 32}{c |}{col 44}    Robust
{col 1}                     w19_retro{col 32}{c |}      Coef.{col 44}   Std. Err.{col 56}      z{col 64}   P>|z|{col 72}     [95% Con{col 85}f. Interval]
{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 17}partisan_9rev {c |}
{space 18}In-Partisan  {c |}{col 32}{res}{space 2} 1.157434{col 44}{space 2}  .196058{col 55}{space 1}    5.90{col 64}{space 3}0.000{col 72}{space 4} .7731673{col 85}{space 3} 1.541701
{txt}{space 16}disagree_total {c |}{col 32}{res}{space 2} .0988018{col 44}{space 2}  .069645{col 55}{space 1}    1.42{col 64}{space 3}0.156{col 72}{space 4}-.0376998{col 85}{space 3} .2353033
{txt}{space 30} {c |}
partisan_9rev#c.disagree_total {c |}
{space 18}In-Partisan  {c |}{col 32}{res}{space 2}-.1356214{col 44}{space 2} .0863302{col 55}{space 1}   -1.57{col 64}{space 3}0.116{col 72}{space 4}-.3048256{col 85}{space 3} .0335828
{txt}{space 30} {c |}
{space 22}numgiven {c |}{col 32}{res}{space 2} .0849052{col 44}{space 2} .0357998{col 55}{space 1}    2.37{col 64}{space 3}0.018{col 72}{space 4} .0147388{col 85}{space 3} .1550716
{txt}{space 14}network_interest {c |}{col 32}{res}{space 2}-.0690997{col 44}{space 2} .1049553{col 55}{space 1}   -0.66{col 64}{space 3}0.510{col 72}{space 4}-.2748083{col 85}{space 3}  .136609
{txt}{space 19}interest_w9 {c |}{col 32}{res}{space 2}-.0569322{col 44}{space 2} .1092475{col 55}{space 1}   -0.52{col 64}{space 3}0.602{col 72}{space 4}-.2710535{col 85}{space 3} .1571891
{txt}{space 27}age {c |}{col 32}{res}{space 2} -.020602{col 44}{space 2}  .007438{col 55}{space 1}   -2.77{col 64}{space 3}0.006{col 72}{space 4}-.0351801{col 85}{space 3}-.0060238
{txt}{space 26}educ {c |}{col 32}{res}{space 2} .0897677{col 44}{space 2} .1078229{col 55}{space 1}    0.83{col 64}{space 3}0.405{col 72}{space 4}-.1215613{col 85}{space 3} .3010967
{txt}{space 24}income {c |}{col 32}{res}{space 2}  .039827{col 44}{space 2} .0265522{col 55}{space 1}    1.50{col 64}{space 3}0.134{col 72}{space 4}-.0122143{col 85}{space 3} .0918684
{txt}{space 30} {c |}
{space 24}gender {c |}
{space 23}Female  {c |}{col 32}{res}{space 2}-.0508152{col 44}{space 2} .1699774{col 55}{space 1}   -0.30{col 64}{space 3}0.765{col 72}{space 4}-.3839647{col 85}{space 3} .2823344
{txt}{space 30} {c |}
{space 22}race_eth {c |}
{space 24}Black  {c |}{col 32}{res}{space 2} .6371973{col 44}{space 2} .2330294{col 55}{space 1}    2.73{col 64}{space 3}0.006{col 72}{space 4} .1804681{col 85}{space 3} 1.093927
{txt}{space 21}Hispanic  {c |}{col 32}{res}{space 2} .1799402{col 44}{space 2}  .383539{col 55}{space 1}    0.47{col 64}{space 3}0.639{col 72}{space 4}-.5717823{col 85}{space 3} .9316628
{txt}{space 24}Other  {c |}{col 32}{res}{space 2}-.0919644{col 44}{space 2} .3982992{col 55}{space 1}   -0.23{col 64}{space 3}0.817{col 72}{space 4}-.8726165{col 85}{space 3} .6886877
{txt}{space 30} {c |}
{space 21}empl_w11a {c |}
{space 19}Unemployed  {c |}{col 32}{res}{space 2}-.2525054{col 44}{space 2} .4535999{col 55}{space 1}   -0.56{col 64}{space 3}0.578{col 72}{space 4}-1.141545{col 85}{space 3}  .636534
{txt}{space 22}Retired  {c |}{col 32}{res}{space 2} .5590854{col 44}{space 2} .2457501{col 55}{space 1}    2.28{col 64}{space 3}0.023{col 72}{space 4} .0774241{col 85}{space 3} 1.040747
{txt}{space 3}Disabled/Not Working/Other  {c |}{col 32}{res}{space 2} .0194285{col 44}{space 2} .2818977{col 55}{space 1}    0.07{col 64}{space 3}0.945{col 72}{space 4}-.5330809{col 85}{space 3} .5719378
{txt}{space 30} {c |}
{space 23}marital {c |}
{space 22}Married  {c |}{col 32}{res}{space 2}-.0278536{col 44}{space 2}  .178684{col 55}{space 1}   -0.16{col 64}{space 3}0.876{col 72}{space 4}-.3780678{col 85}{space 3} .3223607
{txt}{space 26}nfc1 {c |}{col 32}{res}{space 2}-.1222553{col 44}{space 2} .1270105{col 55}{space 1}   -0.96{col 64}{space 3}0.336{col 72}{space 4}-.3711913{col 85}{space 3} .1266807
{txt}{space 21}evaluate1 {c |}{col 32}{res}{space 2} .0736036{col 44}{space 2} .2153731{col 55}{space 1}    0.34{col 64}{space 3}0.733{col 72}{space 4}  -.34852{col 85}{space 3} .4957271
{txt}{hline 31}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                         /cut1 {c |}{col 32}{res}{space 2} .8176427{col 44}{space 2} .6217872{col 72}{space 4}-.4010378{col 85}{space 3} 2.036323
{txt}                         /cut2 {c |}{col 32}{res}{space 2} 2.805719{col 44}{space 2} .6316533{col 72}{space 4} 1.567701{col 85}{space 3} 4.043736
{txt}{hline 31}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est1{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1173.9004}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1081.4199}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1079.8673}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1079.8643}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1079.8643}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}      1352
{txt}{col 51}Wald chi2({res}19{txt}){col 67}= {res}    112.26
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-1079.8643{txt}{col 51}Pseudo R2{col 67}= {res}    0.0801

{txt}{hline 29}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 30}{c |}{col 42}    Robust
{col 1}                   w19_retro{col 30}{c |}      Coef.{col 42}   Std. Err.{col 54}      z{col 62}   P>|z|{col 70}     [95% Con{col 83}f. Interval]
{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 15}partisan_9rev {c |}
{space 16}In-Partisan  {c |}{col 30}{res}{space 2} 1.158948{col 42}{space 2}  .194967{col 53}{space 1}    5.94{col 62}{space 3}0.000{col 70}{space 4} .7768198{col 83}{space 3} 1.541077
{txt}{space 16}disagree_avg {c |}{col 30}{res}{space 2} .2726571{col 42}{space 2} .1919631{col 53}{space 1}    1.42{col 62}{space 3}0.156{col 70}{space 4}-.1035836{col 83}{space 3} .6488978
{txt}{space 28} {c |}
partisan_9rev#c.disagree_avg {c |}
{space 16}In-Partisan  {c |}{col 30}{res}{space 2}-.3968454{col 42}{space 2} .2393941{col 53}{space 1}   -1.66{col 62}{space 3}0.097{col 70}{space 4}-.8660491{col 83}{space 3} .0723583
{txt}{space 28} {c |}
{space 20}numgiven {c |}{col 30}{res}{space 2} .0850328{col 42}{space 2} .0358411{col 53}{space 1}    2.37{col 62}{space 3}0.018{col 70}{space 4} .0147856{col 83}{space 3} .1552801
{txt}{space 12}network_interest {c |}{col 30}{res}{space 2}-.0698774{col 42}{space 2} .1052662{col 53}{space 1}   -0.66{col 62}{space 3}0.507{col 70}{space 4}-.2761952{col 83}{space 3} .1364405
{txt}{space 17}interest_w9 {c |}{col 30}{res}{space 2} -.057595{col 42}{space 2} .1093945{col 53}{space 1}   -0.53{col 62}{space 3}0.599{col 70}{space 4}-.2720042{col 83}{space 3} .1568142
{txt}{space 25}age {c |}{col 30}{res}{space 2}-.0206551{col 42}{space 2} .0074327{col 53}{space 1}   -2.78{col 62}{space 3}0.005{col 70}{space 4}-.0352228{col 83}{space 3}-.0060873
{txt}{space 24}educ {c |}{col 30}{res}{space 2} .0910671{col 42}{space 2} .1077827{col 53}{space 1}    0.84{col 62}{space 3}0.398{col 70}{space 4} -.120183{col 83}{space 3} .3023173
{txt}{space 22}income {c |}{col 30}{res}{space 2} .0408091{col 42}{space 2} .0264749{col 53}{space 1}    1.54{col 62}{space 3}0.123{col 70}{space 4}-.0110808{col 83}{space 3} .0926989
{txt}{space 28} {c |}
{space 22}gender {c |}
{space 21}Female  {c |}{col 30}{res}{space 2}-.0516051{col 42}{space 2} .1696888{col 53}{space 1}   -0.30{col 62}{space 3}0.761{col 70}{space 4}-.3841889{col 83}{space 3} .2809788
{txt}{space 28} {c |}
{space 20}race_eth {c |}
{space 22}Black  {c |}{col 30}{res}{space 2} .6337496{col 42}{space 2}  .231481{col 53}{space 1}    2.74{col 62}{space 3}0.006{col 70}{space 4} .1800552{col 83}{space 3} 1.087444
{txt}{space 19}Hispanic  {c |}{col 30}{res}{space 2} .1739726{col 42}{space 2} .3816058{col 53}{space 1}    0.46{col 62}{space 3}0.648{col 70}{space 4} -.573961{col 83}{space 3} .9219062
{txt}{space 22}Other  {c |}{col 30}{res}{space 2}-.0786675{col 42}{space 2} .4001864{col 53}{space 1}   -0.20{col 62}{space 3}0.844{col 70}{space 4}-.8630185{col 83}{space 3} .7056835
{txt}{space 28} {c |}
{space 19}empl_w11a {c |}
{space 17}Unemployed  {c |}{col 30}{res}{space 2}-.2360886{col 42}{space 2} .4558653{col 53}{space 1}   -0.52{col 62}{space 3}0.605{col 70}{space 4}-1.129568{col 83}{space 3}  .657391
{txt}{space 20}Retired  {c |}{col 30}{res}{space 2} .5628375{col 42}{space 2} .2460772{col 53}{space 1}    2.29{col 62}{space 3}0.022{col 70}{space 4} .0805351{col 83}{space 3}  1.04514
{txt}{space 1}Disabled/Not Working/Other  {c |}{col 30}{res}{space 2} .0268027{col 42}{space 2} .2815171{col 53}{space 1}    0.10{col 62}{space 3}0.924{col 70}{space 4}-.5249607{col 83}{space 3} .5785661
{txt}{space 28} {c |}
{space 21}marital {c |}
{space 20}Married  {c |}{col 30}{res}{space 2}-.0334752{col 42}{space 2} .1790885{col 53}{space 1}   -0.19{col 62}{space 3}0.852{col 70}{space 4}-.3844822{col 83}{space 3} .3175319
{txt}{space 24}nfc1 {c |}{col 30}{res}{space 2}-.1261543{col 42}{space 2} .1270552{col 53}{space 1}   -0.99{col 62}{space 3}0.321{col 70}{space 4} -.375178{col 83}{space 3} .1228693
{txt}{space 19}evaluate1 {c |}{col 30}{res}{space 2} .0776308{col 42}{space 2}  .214936{col 53}{space 1}    0.36{col 62}{space 3}0.718{col 70}{space 4} -.343636{col 83}{space 3} .4988975
{txt}{hline 29}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                       /cut1 {c |}{col 30}{res}{space 2} .8309601{col 42}{space 2} .6180086{col 70}{space 4}-.3803145{col 83}{space 3} 2.042235
{txt}                       /cut2 {c |}{col 30}{res}{space 2} 2.819549{col 42}{space 2} .6281975{col 70}{space 4} 1.588305{col 83}{space 3} 4.050794
{txt}{hline 29}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est2{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1173.9004}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1078.9287}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1077.2211}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1077.2173}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1077.2173}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}      1352
{txt}{col 51}Wald chi2({res}19{txt}){col 67}= {res}    115.23
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-1077.2173{txt}{col 51}Pseudo R2{col 67}= {res}    0.0824

{txt}{hline 35}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 36}{c |}{col 48}    Robust
{col 1}                         w19_retro{col 36}{c |}      Coef.{col 48}   Std. Err.{col 60}      z{col 68}   P>|z|{col 76}     [95% Con{col 89}f. Interval]
{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 21}partisan_9rev {c |}
{space 22}In-Partisan  {c |}{col 36}{res}{space 2} 1.084026{col 48}{space 2}  .195847{col 59}{space 1}    5.54{col 68}{space 3}0.000{col 76}{space 4}  .700173{col 89}{space 3} 1.467879
{txt}{space 16}disagree_total_int {c |}{col 36}{res}{space 2} .0352061{col 48}{space 2} .0174154{col 59}{space 1}    2.02{col 68}{space 3}0.043{col 76}{space 4} .0010725{col 89}{space 3} .0693398
{txt}{space 34} {c |}
partisan_9rev#c.disagree_total_int {c |}
{space 22}In-Partisan  {c |}{col 36}{res}{space 2}-.0533563{col 48}{space 2} .0213676{col 59}{space 1}   -2.50{col 68}{space 3}0.013{col 76}{space 4}-.0952359{col 89}{space 3}-.0114766
{txt}{space 34} {c |}
{space 26}numgiven {c |}{col 36}{res}{space 2} .0854978{col 48}{space 2} .0359996{col 59}{space 1}    2.37{col 68}{space 3}0.018{col 76}{space 4} .0149399{col 89}{space 3} .1560556
{txt}{space 18}network_interest {c |}{col 36}{res}{space 2}-.0764768{col 48}{space 2} .1062077{col 59}{space 1}   -0.72{col 68}{space 3}0.471{col 76}{space 4}-.2846401{col 89}{space 3} .1316864
{txt}{space 23}interest_w9 {c |}{col 36}{res}{space 2}-.0556569{col 48}{space 2}   .10866{col 59}{space 1}   -0.51{col 68}{space 3}0.609{col 76}{space 4}-.2686265{col 89}{space 3} .1573128
{txt}{space 31}age {c |}{col 36}{res}{space 2}-.0209175{col 48}{space 2} .0074107{col 59}{space 1}   -2.82{col 68}{space 3}0.005{col 76}{space 4}-.0354422{col 89}{space 3}-.0063928
{txt}{space 30}educ {c |}{col 36}{res}{space 2} .0865696{col 48}{space 2} .1070694{col 59}{space 1}    0.81{col 68}{space 3}0.419{col 76}{space 4}-.1232826{col 89}{space 3} .2964218
{txt}{space 28}income {c |}{col 36}{res}{space 2} .0403499{col 48}{space 2}  .026541{col 59}{space 1}    1.52{col 68}{space 3}0.128{col 76}{space 4}-.0116696{col 89}{space 3} .0923693
{txt}{space 34} {c |}
{space 28}gender {c |}
{space 27}Female  {c |}{col 36}{res}{space 2}-.0505965{col 48}{space 2} .1702347{col 59}{space 1}   -0.30{col 68}{space 3}0.766{col 76}{space 4}-.3842503{col 89}{space 3} .2830573
{txt}{space 34} {c |}
{space 26}race_eth {c |}
{space 28}Black  {c |}{col 36}{res}{space 2} .5950465{col 48}{space 2} .2373517{col 59}{space 1}    2.51{col 68}{space 3}0.012{col 76}{space 4} .1298458{col 89}{space 3} 1.060247
{txt}{space 25}Hispanic  {c |}{col 36}{res}{space 2} .1785769{col 48}{space 2} .3820208{col 59}{space 1}    0.47{col 68}{space 3}0.640{col 76}{space 4}-.5701701{col 89}{space 3}  .927324
{txt}{space 28}Other  {c |}{col 36}{res}{space 2}-.0729249{col 48}{space 2}  .409096{col 59}{space 1}   -0.18{col 68}{space 3}0.859{col 76}{space 4}-.8747382{col 89}{space 3} .7288885
{txt}{space 34} {c |}
{space 25}empl_w11a {c |}
{space 23}Unemployed  {c |}{col 36}{res}{space 2}-.2232858{col 48}{space 2} .4611097{col 59}{space 1}   -0.48{col 68}{space 3}0.628{col 76}{space 4}-1.127044{col 89}{space 3} .6804725
{txt}{space 26}Retired  {c |}{col 36}{res}{space 2} .5635373{col 48}{space 2} .2456618{col 59}{space 1}    2.29{col 68}{space 3}0.022{col 76}{space 4} .0820489{col 89}{space 3} 1.045026
{txt}{space 7}Disabled/Not Working/Other  {c |}{col 36}{res}{space 2} .0236838{col 48}{space 2} .2809287{col 59}{space 1}    0.08{col 68}{space 3}0.933{col 76}{space 4}-.5269264{col 89}{space 3} .5742941
{txt}{space 34} {c |}
{space 27}marital {c |}
{space 26}Married  {c |}{col 36}{res}{space 2}-.0302764{col 48}{space 2} .1787809{col 59}{space 1}   -0.17{col 68}{space 3}0.866{col 76}{space 4}-.3806806{col 89}{space 3} .3201277
{txt}{space 30}nfc1 {c |}{col 36}{res}{space 2}-.1280767{col 48}{space 2} .1272583{col 59}{space 1}   -1.01{col 68}{space 3}0.314{col 76}{space 4}-.3774983{col 89}{space 3} .1213449
{txt}{space 25}evaluate1 {c |}{col 36}{res}{space 2} .0611319{col 48}{space 2} .2142629{col 59}{space 1}    0.29{col 68}{space 3}0.775{col 76}{space 4}-.3588156{col 89}{space 3} .4810794
{txt}{hline 35}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                             /cut1 {c |}{col 36}{res}{space 2} .7196555{col 48}{space 2}   .63497{col 76}{space 4}-.5248629{col 89}{space 3} 1.964174
{txt}                             /cut2 {c |}{col 36}{res}{space 2} 2.713021{col 48}{space 2} .6434978{col 76}{space 4} 1.451789{col 89}{space 3} 3.974254
{txt}{hline 35}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est3{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1173.9004}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1079.2457}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1077.5339}  
Iteration 3:{space 3}log pseudolikelihood = {res:  -1077.53}  
Iteration 4:{space 3}log pseudolikelihood = {res:  -1077.53}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}      1352
{txt}{col 51}Wald chi2({res}19{txt}){col 67}= {res}    113.85
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}  -1077.53{txt}{col 51}Pseudo R2{col 67}= {res}    0.0821

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}                  w19_retro{col 29}{c |}      Coef.{col 41}   Std. Err.{col 53}      z{col 61}   P>|z|{col 69}     [95% Con{col 82}f. Interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}partisan_9rev {c |}
{space 15}In-Partisan  {c |}{col 29}{res}{space 2} 1.099026{col 41}{space 2}  .196597{col 52}{space 1}    5.59{col 61}{space 3}0.000{col 69}{space 4} .7137025{col 82}{space 3} 1.484349
{txt}{space 7}disagree_total_close {c |}{col 29}{res}{space 2} .0350305{col 41}{space 2} .0167828{col 52}{space 1}    2.09{col 61}{space 3}0.037{col 69}{space 4} .0021369{col 82}{space 3} .0679241
{txt}{space 27} {c |}
{space 14}partisan_9rev#{c |}
{space 5}c.disagree_total_close {c |}
{space 15}In-Partisan  {c |}{col 29}{res}{space 2} -.046882{col 41}{space 2} .0206859{col 52}{space 1}   -2.27{col 61}{space 3}0.023{col 69}{space 4}-.0874256{col 82}{space 3}-.0063385
{txt}{space 27} {c |}
{space 19}numgiven {c |}{col 29}{res}{space 2} .0850178{col 41}{space 2} .0357159{col 52}{space 1}    2.38{col 61}{space 3}0.017{col 69}{space 4}  .015016{col 82}{space 3} .1550196
{txt}{space 11}network_interest {c |}{col 29}{res}{space 2}-.0661227{col 41}{space 2} .1045735{col 52}{space 1}   -0.63{col 61}{space 3}0.527{col 69}{space 4} -.271083{col 82}{space 3} .1388377
{txt}{space 16}interest_w9 {c |}{col 29}{res}{space 2}-.0553059{col 41}{space 2} .1088128{col 52}{space 1}   -0.51{col 61}{space 3}0.611{col 69}{space 4}-.2685751{col 82}{space 3} .1579632
{txt}{space 24}age {c |}{col 29}{res}{space 2}-.0210401{col 41}{space 2} .0073852{col 52}{space 1}   -2.85{col 61}{space 3}0.004{col 69}{space 4}-.0355149{col 82}{space 3}-.0065653
{txt}{space 23}educ {c |}{col 29}{res}{space 2} .0889474{col 41}{space 2} .1074599{col 52}{space 1}    0.83{col 61}{space 3}0.408{col 69}{space 4}-.1216702{col 82}{space 3}  .299565
{txt}{space 21}income {c |}{col 29}{res}{space 2} .0403516{col 41}{space 2} .0265004{col 52}{space 1}    1.52{col 61}{space 3}0.128{col 69}{space 4}-.0115883{col 82}{space 3} .0922914
{txt}{space 27} {c |}
{space 21}gender {c |}
{space 20}Female  {c |}{col 29}{res}{space 2}-.0385218{col 41}{space 2} .1702421{col 52}{space 1}   -0.23{col 61}{space 3}0.821{col 69}{space 4}-.3721903{col 82}{space 3} .2951466
{txt}{space 27} {c |}
{space 19}race_eth {c |}
{space 21}Black  {c |}{col 29}{res}{space 2} .6224633{col 41}{space 2} .2320785{col 52}{space 1}    2.68{col 61}{space 3}0.007{col 69}{space 4} .1675978{col 82}{space 3} 1.077329
{txt}{space 18}Hispanic  {c |}{col 29}{res}{space 2} .1631831{col 41}{space 2} .3807295{col 52}{space 1}    0.43{col 61}{space 3}0.668{col 69}{space 4} -.583033{col 82}{space 3} .9093993
{txt}{space 21}Other  {c |}{col 29}{res}{space 2}-.1023378{col 41}{space 2} .4084681{col 52}{space 1}   -0.25{col 61}{space 3}0.802{col 69}{space 4}-.9029206{col 82}{space 3} .6982451
{txt}{space 27} {c |}
{space 18}empl_w11a {c |}
{space 16}Unemployed  {c |}{col 29}{res}{space 2}-.2729743{col 41}{space 2} .4441507{col 52}{space 1}   -0.61{col 61}{space 3}0.539{col 69}{space 4}-1.143494{col 82}{space 3} .5975452
{txt}{space 19}Retired  {c |}{col 29}{res}{space 2} .5591284{col 41}{space 2} .2440773{col 52}{space 1}    2.29{col 61}{space 3}0.022{col 69}{space 4} .0807456{col 82}{space 3} 1.037511
{txt}Disabled/Not Working/Other  {c |}{col 29}{res}{space 2} .0200613{col 41}{space 2} .2808614{col 52}{space 1}    0.07{col 61}{space 3}0.943{col 69}{space 4} -.530417{col 82}{space 3} .5705396
{txt}{space 27} {c |}
{space 20}marital {c |}
{space 19}Married  {c |}{col 29}{res}{space 2}-.0186836{col 41}{space 2} .1779563{col 52}{space 1}   -0.10{col 61}{space 3}0.916{col 69}{space 4}-.3674714{col 82}{space 3} .3301043
{txt}{space 23}nfc1 {c |}{col 29}{res}{space 2}-.1231851{col 41}{space 2} .1268428{col 52}{space 1}   -0.97{col 61}{space 3}0.331{col 69}{space 4}-.3717925{col 82}{space 3} .1254222
{txt}{space 18}evaluate1 {c |}{col 29}{res}{space 2} .0714914{col 41}{space 2} .2140743{col 52}{space 1}    0.33{col 61}{space 3}0.738{col 69}{space 4}-.3480865{col 82}{space 3} .4910693
{txt}{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                      /cut1 {c |}{col 29}{res}{space 2} .7793685{col 41}{space 2} .6198786{col 69}{space 4}-.4355712{col 82}{space 3} 1.994308
{txt}                      /cut2 {c |}{col 29}{res}{space 2} 2.771438{col 41}{space 2} .6296446{col 69}{space 4} 1.537358{col 82}{space 3} 4.005519
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est4{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1173.9004}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1079.9474}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1078.2763}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1078.2726}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1078.2726}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}      1352
{txt}{col 51}Wald chi2({res}19{txt}){col 67}= {res}    114.50
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-1078.2726{txt}{col 51}Pseudo R2{col 67}= {res}    0.0815

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}                  w19_retro{col 29}{c |}      Coef.{col 41}   Std. Err.{col 53}      z{col 61}   P>|z|{col 69}     [95% Con{col 82}f. Interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}partisan_9rev {c |}
{space 15}In-Partisan  {c |}{col 29}{res}{space 2} 1.038144{col 41}{space 2} .2062524{col 52}{space 1}    5.03{col 61}{space 3}0.000{col 69}{space 4} .6338969{col 82}{space 3} 1.442391
{txt}{space 6}disagree_total_weight {c |}{col 29}{res}{space 2} .0351483{col 41}{space 2} .0176643{col 52}{space 1}    1.99{col 61}{space 3}0.047{col 69}{space 4} .0005269{col 82}{space 3} .0697697
{txt}{space 27} {c |}
{space 14}partisan_9rev#{c |}
{space 4}c.disagree_total_weight {c |}
{space 15}In-Partisan  {c |}{col 29}{res}{space 2}-.0496423{col 41}{space 2} .0218888{col 52}{space 1}   -2.27{col 61}{space 3}0.023{col 69}{space 4}-.0925435{col 82}{space 3} -.006741
{txt}{space 27} {c |}
{space 19}numgiven {c |}{col 29}{res}{space 2} .0844099{col 41}{space 2} .0361022{col 52}{space 1}    2.34{col 61}{space 3}0.019{col 69}{space 4} .0136509{col 82}{space 3} .1551689
{txt}{space 11}network_interest {c |}{col 29}{res}{space 2}-.0691313{col 41}{space 2} .1044241{col 52}{space 1}   -0.66{col 61}{space 3}0.508{col 69}{space 4}-.2737987{col 82}{space 3} .1355362
{txt}{space 16}interest_w9 {c |}{col 29}{res}{space 2}-.0535489{col 41}{space 2} .1089597{col 52}{space 1}   -0.49{col 61}{space 3}0.623{col 69}{space 4} -.267106{col 82}{space 3} .1600081
{txt}{space 24}age {c |}{col 29}{res}{space 2} -.020971{col 41}{space 2} .0074641{col 52}{space 1}   -2.81{col 61}{space 3}0.005{col 69}{space 4}-.0356004{col 82}{space 3}-.0063415
{txt}{space 23}educ {c |}{col 29}{res}{space 2} .0931132{col 41}{space 2} .1075932{col 52}{space 1}    0.87{col 61}{space 3}0.387{col 69}{space 4}-.1177656{col 82}{space 3}  .303992
{txt}{space 21}income {c |}{col 29}{res}{space 2} .0392282{col 41}{space 2} .0265555{col 52}{space 1}    1.48{col 61}{space 3}0.140{col 69}{space 4}-.0128197{col 82}{space 3} .0912761
{txt}{space 27} {c |}
{space 21}gender {c |}
{space 20}Female  {c |}{col 29}{res}{space 2}-.0485944{col 41}{space 2} .1706569{col 52}{space 1}   -0.28{col 61}{space 3}0.776{col 69}{space 4}-.3830757{col 82}{space 3} .2858869
{txt}{space 27} {c |}
{space 19}race_eth {c |}
{space 21}Black  {c |}{col 29}{res}{space 2} .6189951{col 41}{space 2}  .233668{col 52}{space 1}    2.65{col 61}{space 3}0.008{col 69}{space 4} .1610142{col 82}{space 3} 1.076976
{txt}{space 18}Hispanic  {c |}{col 29}{res}{space 2} .1683163{col 41}{space 2} .3819983{col 52}{space 1}    0.44{col 61}{space 3}0.659{col 69}{space 4}-.5803867{col 82}{space 3} .9170193
{txt}{space 21}Other  {c |}{col 29}{res}{space 2} -.082022{col 41}{space 2} .3973557{col 52}{space 1}   -0.21{col 61}{space 3}0.836{col 69}{space 4}-.8608248{col 82}{space 3} .6967807
{txt}{space 27} {c |}
{space 18}empl_w11a {c |}
{space 16}Unemployed  {c |}{col 29}{res}{space 2}-.2422588{col 41}{space 2} .4492929{col 52}{space 1}   -0.54{col 61}{space 3}0.590{col 69}{space 4}-1.122857{col 82}{space 3}  .638339
{txt}{space 19}Retired  {c |}{col 29}{res}{space 2} .5629162{col 41}{space 2} .2461072{col 52}{space 1}    2.29{col 61}{space 3}0.022{col 69}{space 4}  .080555{col 82}{space 3} 1.045277
{txt}Disabled/Not Working/Other  {c |}{col 29}{res}{space 2} .0216666{col 41}{space 2} .2810928{col 52}{space 1}    0.08{col 61}{space 3}0.939{col 69}{space 4}-.5292651{col 82}{space 3} .5725983
{txt}{space 27} {c |}
{space 20}marital {c |}
{space 19}Married  {c |}{col 29}{res}{space 2}-.0231257{col 41}{space 2} .1785055{col 52}{space 1}   -0.13{col 61}{space 3}0.897{col 69}{space 4}-.3729901{col 82}{space 3} .3267388
{txt}{space 23}nfc1 {c |}{col 29}{res}{space 2}-.1216533{col 41}{space 2} .1272164{col 52}{space 1}   -0.96{col 61}{space 3}0.339{col 69}{space 4}-.3709928{col 82}{space 3} .1276862
{txt}{space 18}evaluate1 {c |}{col 29}{res}{space 2} .0663711{col 41}{space 2} .2142549{col 52}{space 1}    0.31{col 61}{space 3}0.757{col 69}{space 4}-.3535608{col 82}{space 3} .4863029
{txt}{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                      /cut1 {c |}{col 29}{res}{space 2} .7274368{col 41}{space 2} .6231166{col 69}{space 4}-.4938493{col 82}{space 3} 1.948723
{txt}                      /cut2 {c |}{col 29}{res}{space 2} 2.718463{col 41}{space 2} .6325996{col 69}{space 4} 1.478591{col 82}{space 3} 3.958336
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est5{txt} stored)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1173.9004}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1078.1085}  
Iteration 2:{space 3}log pseudolikelihood = {res: -1076.315}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1076.3106}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1076.3106}  
{res}
{txt}Ordered logistic regression{col 51}Number of obs{col 67}= {res}      1352
{txt}{col 51}Wald chi2({res}19{txt}){col 67}= {res}    114.71
{txt}{col 51}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-1076.3106{txt}{col 51}Pseudo R2{col 67}= {res}    0.0831

{txt}{hline 28}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 29}{c |}{col 41}    Robust
{col 1}                  w19_retro{col 29}{c |}      Coef.{col 41}   Std. Err.{col 53}      z{col 61}   P>|z|{col 69}     [95% Con{col 82}f. Interval]
{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 14}partisan_9rev {c |}
{space 15}In-Partisan  {c |}{col 29}{res}{space 2} 2.492617{col 41}{space 2} .4756746{col 52}{space 1}    5.24{col 61}{space 3}0.000{col 69}{space 4} 1.560312{col 82}{space 3} 3.424922
{txt}{space 20}gendiff {c |}{col 29}{res}{space 2}  .354603{col 41}{space 2} .1482273{col 52}{space 1}    2.39{col 61}{space 3}0.017{col 69}{space 4} .0640828{col 82}{space 3} .6451231
{txt}{space 27} {c |}
{space 4}partisan_9rev#c.gendiff {c |}
{space 15}In-Partisan  {c |}{col 29}{res}{space 2} -.550651{col 41}{space 2} .1929267{col 52}{space 1}   -2.85{col 61}{space 3}0.004{col 69}{space 4}-.9287805{col 82}{space 3}-.1725215
{txt}{space 27} {c |}
{space 19}numgiven {c |}{col 29}{res}{space 2} .0772635{col 41}{space 2} .0360914{col 52}{space 1}    2.14{col 61}{space 3}0.032{col 69}{space 4} .0065256{col 82}{space 3} .1480014
{txt}{space 11}network_interest {c |}{col 29}{res}{space 2}-.0850465{col 41}{space 2} .1018599{col 52}{space 1}   -0.83{col 61}{space 3}0.404{col 69}{space 4}-.2846882{col 82}{space 3} .1145952
{txt}{space 16}interest_w9 {c |}{col 29}{res}{space 2}-.0472301{col 41}{space 2} .1074979{col 52}{space 1}   -0.44{col 61}{space 3}0.660{col 69}{space 4}-.2579222{col 82}{space 3}  .163462
{txt}{space 24}age {c |}{col 29}{res}{space 2}-.0222312{col 41}{space 2}  .007617{col 52}{space 1}   -2.92{col 61}{space 3}0.004{col 69}{space 4}-.0371603{col 82}{space 3}-.0073022
{txt}{space 23}educ {c |}{col 29}{res}{space 2} .1085333{col 41}{space 2} .1070215{col 52}{space 1}    1.01{col 61}{space 3}0.311{col 69}{space 4}-.1012249{col 82}{space 3} .3182916
{txt}{space 21}income {c |}{col 29}{res}{space 2} .0383579{col 41}{space 2}  .026579{col 52}{space 1}    1.44{col 61}{space 3}0.149{col 69}{space 4} -.013736{col 82}{space 3} .0904519
{txt}{space 27} {c |}
{space 21}gender {c |}
{space 20}Female  {c |}{col 29}{res}{space 2}-.0633537{col 41}{space 2} .1717746{col 52}{space 1}   -0.37{col 61}{space 3}0.712{col 69}{space 4}-.4000258{col 82}{space 3} .2733184
{txt}{space 27} {c |}
{space 19}race_eth {c |}
{space 21}Black  {c |}{col 29}{res}{space 2} .6551385{col 41}{space 2} .2266873{col 52}{space 1}    2.89{col 61}{space 3}0.004{col 69}{space 4} .2108396{col 82}{space 3} 1.099437
{txt}{space 18}Hispanic  {c |}{col 29}{res}{space 2} .2073999{col 41}{space 2} .3941418{col 52}{space 1}    0.53{col 61}{space 3}0.599{col 69}{space 4}-.5651039{col 82}{space 3} .9799036
{txt}{space 21}Other  {c |}{col 29}{res}{space 2}-.0666427{col 41}{space 2} .3730701{col 52}{space 1}   -0.18{col 61}{space 3}0.858{col 69}{space 4}-.7978467{col 82}{space 3} .6645613
{txt}{space 27} {c |}
{space 18}empl_w11a {c |}
{space 16}Unemployed  {c |}{col 29}{res}{space 2}-.2435294{col 41}{space 2} .4493956{col 52}{space 1}   -0.54{col 61}{space 3}0.588{col 69}{space 4}-1.124329{col 82}{space 3} .6372697
{txt}{space 19}Retired  {c |}{col 29}{res}{space 2} .5829204{col 41}{space 2} .2488636{col 52}{space 1}    2.34{col 61}{space 3}0.019{col 69}{space 4} .0951566{col 82}{space 3} 1.070684
{txt}Disabled/Not Working/Other  {c |}{col 29}{res}{space 2} .0217594{col 41}{space 2} .2764387{col 52}{space 1}    0.08{col 61}{space 3}0.937{col 69}{space 4}-.5200505{col 82}{space 3} .5635693
{txt}{space 27} {c |}
{space 20}marital {c |}
{space 19}Married  {c |}{col 29}{res}{space 2}-.0244064{col 41}{space 2} .1783079{col 52}{space 1}   -0.14{col 61}{space 3}0.891{col 69}{space 4}-.3738834{col 82}{space 3} .3250706
{txt}{space 23}nfc1 {c |}{col 29}{res}{space 2}-.1403026{col 41}{space 2} .1271286{col 52}{space 1}   -1.10{col 61}{space 3}0.270{col 69}{space 4}-.3894701{col 82}{space 3} .1088648
{txt}{space 18}evaluate1 {c |}{col 29}{res}{space 2} .0865042{col 41}{space 2} .2135859{col 52}{space 1}    0.41{col 61}{space 3}0.685{col 69}{space 4}-.3321165{col 82}{space 3} .5051249
{txt}{hline 28}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
                      /cut1 {c |}{col 29}{res}{space 2} 1.573667{col 41}{space 2} .6971007{col 69}{space 4} .2073743{col 82}{space 3} 2.939959
{txt}                      /cut2 {c |}{col 29}{res}{space 2}  3.56833{col 41}{space 2} .6978302{col 69}{space 4} 2.200608{col 82}{space 3} 4.936052
{txt}{hline 28}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
({res}est6{txt} stored)

{com}. 
. esttab using 2008ECON_ALTMEASURES_JULY.rtf, onecell nobaselevels replace label pr2 aic bic se star(+ 0.10 * 0.05 ** 0.01) ///
>         mtitles("Original Measure" "(/D+A)" "Weigh: by Disc. Interest" ///
>                 "Weigh: by Tie Strength" "Weighted by Gen Dis" "Gen Disagree")  ///
>         title({c -(}\b Table XX.{c )-} "Alternative Measures of Disagreement - 2008-2009 ANES (Econ: July)") ///
>         rename(disagree_total Disagreement disagree_avg "Disagreement" disagree_total_int Disagreement disagree_total_close Disagreement ///
>                                 disagree_total_weight Disagreement gendiff Disagreement ///
>                                 1.partisan_9rev#c.disagree_total Partisan*Disagree ///
>                                 1.partisan_9rev#c.disagree_avg Partisan*Disagree ///
>                                 1.partisan_9rev#c.disagree_total_int Partisan*Disagree ///
>                                 1.partisan_9rev#c.disagree_total_close Partisan*Disagree ///
>                                 1.partisan_9rev#c.disagree_total_weight Partisan*Disagree ///
>                                 1.partisan_9rev#c.gendiff Partisan*Disagree)
{res}{txt}(output written to {browse  `"2008ECON_ALTMEASURES_JULY.rtf"'})

{com}. 
.                                 
. eststo clear
{txt}
{com}. 
. 
. 
. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\Joshua\Dropbox\Work\Networks and Economic Perceptions\Data and Do Files\Online Appendix D Output.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res} 6 Jun 2018, 11:00:57
{txt}{.-}
{smcl}
{txt}{sf}{ul off}